From f390884f670ad3f350eb13cf4c0afb3eb77e4154 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Tue, 4 Aug 2026 15:25:32 +0200 Subject: [PATCH 01/30] Add v0.3.0 design doc: Stage-2 autoregressive redesign MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Design contract for the v0.3.0 config break and network.py refactor, following the 2026-08-04 meeting with Jan. Not implemented yet. The 2026-08-03 WGAN rollout benchmark failed specifically at the secondary-species level (zero photons, ~4M hallucinated -14 muon antineutrinos). The response pivots Stage 2 to autoregressive generation in descending-energy order with teacher forcing, and reverts the particle type to a categorical representation. That needs a config break: [conditioning] / [stage1_model] / [stage2_model] / [train] replace the single global train.mode and [model] block, so per-stage generators (stage1 flow + stage2 wgan), stage-2-only training, and one-shot-vs-autoregressive comparison all become expressible. The particle and material conditioning axes are configured independently and mix freely, each with physical / embedding / onehot modes; the stage-2 type target mirrors the same three names, with conditioning.particle.emb_dim sizing both so the two share one class map. network.py collapses from ten permutation classes (stage x objective x routed) into composable parts — encoder x trunk x objective — which also makes routed WGAN work for the first time; it was only ever rejected because no routed WGAN generator class existed. The doc specifies every config option, the v0.2 migration (shim for both configs and checkpoints, gated on a bit-identical output diff), the refactor, and the implementation order. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 2 + docs/v0.3.0-design.md | 932 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 934 insertions(+) create mode 100644 docs/v0.3.0-design.md diff --git a/CLAUDE.md b/CLAUDE.md index ccab334..c3cc181 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -91,4 +91,6 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). +**v0.3.0 — Stage-2 autoregressive redesign (designed, not implemented; branch `v0.3.0-stage2-autoregressive`):** the 2026-08-03 WGAN rollout benchmark failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). The agreed response pivots Stage 2 to **autoregressive generation** in descending-energy order with teacher forcing, and switches the particle-type representation back to **categorical** (top N−1 by training-set count + an "other" bucket), reversing the 2026-07-17 continuous `(log-mass, charge)` target. This requires a config break: `[conditioning]` / `[stage1_model]` / `[stage2_model]` / `[train]` blocks replace the single global `train.mode` + `[model]`, so per-stage generators (`stage1 = flow` + `stage2 = wgan`), stage-2-only training, and one-shot-vs-autoregressive comparison are all expressible. `network.py` is refactored from ten permutation classes into composable parts (encoder × trunk × objective), which also makes routed WGAN work for the first time. **Full design contract, with every config option documented: `docs/v0.3.0-design.md` — read it before touching `giant/config.py` or `giant/model/network.py`.** + **Condor-submitted GPU training/rollout (in progress, `condor-gpu-train-rollout` branch, not yet merged):** moves `giant train`/`giant rollout` off the shared portal GPU dev machines (see Compute environment) onto remote-GPU HTCondor submission on TOpAS/NEMO2 (`giant/condor.py`). Partway between "needs major features" and feature-complete — not ready to merge yet. diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md new file mode 100644 index 0000000..5b4408d --- /dev/null +++ b/docs/v0.3.0-design.md @@ -0,0 +1,932 @@ +# GIANT v0.3.0 — Stage-2 autoregressive redesign + +**Status:** design agreed, not implemented. Branch `v0.3.0-stage2-autoregressive`. +**Date:** 2026-08-04. +**Source:** `~/knowledge-base/meetings/2026-08-04-jan-stage2-autoregressive-architecture.md` +(meeting with Jan), plus the design decisions taken in the session that produced +this document. + +This document is the implementation contract for v0.3.0. It specifies the new +config format option by option, the `network.py` refactor, and the order in which +to build it. Read it before touching `giant/config.py` or `giant/model/network.py`. + +--- + +## Table of contents + +1. [Why](#1-why) +2. [Decisions register](#2-decisions-register) +3. [Config format reference](#3-config-format-reference) +4. [Migration: v0.2 -> v0.3](#4-migration-v02---v03) +5. [network.py refactor](#5-networkpy-refactor) +6. [Stage-2 autoregressive design](#6-stage-2-autoregressive-design) +7. [Training loop](#7-training-loop) +8. [Data and setup-cache changes](#8-data-and-setup-cache-changes) +9. [Config machinery changes](#9-config-machinery-changes) +10. [Callers that need updating](#10-callers-that-need-updating) +11. [Open questions](#11-open-questions) +12. [Implementation order](#12-implementation-order) + +--- + +## 1. Why + +The 2026-08-03 WGAN rollout benchmark +(`~/knowledge-base/experiments/giant-wgan-physical-rollout-validation.md`) was +good at the primary-step level and **failed at the secondary-species level**: +zero photon secondaries generated, ~4M hallucinated `-14` (muon antineutrino) +secondaries — a species essentially absent from Geant4. + +Stage 1 is not implicated; the meeting scoped everything to Stage 2. Two changes +were agreed together: + +- **Autoregressive generation** over secondaries in decreasing energy order, + replacing the one-shot masked `K_MAX=15` prediction, trained with teacher + forcing. +- **Categorical particle type** with a data-derived "other" bucket, reversing the + 2026-07-17 move to a continuous `(log-mass, charge)` target. Working hypothesis: + the continuous target is part of what let the generator collapse onto degenerate + species. + +The meeting also set a **methodology**: compare Stage-2 architectures *standalone* +(trained directly on secondary columns, no Stage-1 forward pass) before chaining +the winner behind Stage 1. Iterating on the compounding-rollout-error problem is +far cheaper that way than paying for a full two-stage run per candidate. + +That methodology is what forces the config refactor: v0.2 has a single global +`train.mode` and a single `[model]` block, with no way to express "train stage 2 +only", "stage 1 flow + stage 2 WGAN", or "one-shot vs autoregressive stage 2". + +--- + +## 2. Decisions register + +| # | Decision | Rationale | +|---|----------|-----------| +| 1 | **`n_sec` head moves from stage 1 to stage 2** | Stage 1 becomes the pure 9D primary step. A stage-2-only run is then self-contained (it can predict its own multiplicity), and §3's "implicit stop token" alternative gets a natural home in the same config block. | +| 2 | **Full mixed per-stage objectives** | `stage1 = flow` + `stage2 = wgan` must actually run — Stage 1 is good as flow, Stage 2 is what is being iterated on. The train loop becomes one trainer object per stage, each owning its optimizers and update cadence. | +| 3 | **Migration shim for configs *and* checkpoints** | Nothing on `/ceph` goes dead. v0.2 `config.toml` files and v0.2 `model_config` dicts are translated on load. | +| 4 | **Nested objective sub-tables** | `[stage1_model.wgan]`, `[stage2_model.ddpm]` rather than flat `wgan_n_critic` keys — self-documenting about which keys the active generator ignores, and validation can warn on a populated sub-table that is never read. | +| 5 | **Particle type is adversarial: straight-through Gumbel into the critic** | The critic sees a relaxed one-hot alongside energy/direction, so the joint (species, kinematics) distribution is learned rather than factorized. Makes the collapse hypothesis directly testable instead of assumed. | +| 6 | **No charge conservation in v0.3.0** | Explicitly "not yet worked out" in the meeting. No `[stage2_model.conservation]` block at all. Energy conservation stays exact and implicit in the stick-breaking encoding. | +| 7 | **Explicit stage-prefixed CLI flags** | `--stage2-hidden-dim` etc., no generic `--set path=value`. Discoverable via `--help` and tab-completable; the cost is a flag list kept in sync with `DEFAULT_CONFIG` by hand. | +| 8 | **AR history is a config axis, markov default** | Markov (previous token + remaining budget + slot index) is the baseline that makes the meeting's §6 "is attention useful" question answerable by ablation rather than by comparing differently-shaped models. Both sit behind one `history_encoder(prefix) -> vector` interface. | + +### 2.1 Consequence of decision 5 + +Straight-through Gumbel needs a critic to receive the relaxed one-hot. So the +type mechanism is **implied by the generator**, and needs no config key of its own: + +| `stage2_model.generator` | Type mechanism | +|--------------------------|----------------| +| `"wgan"` | The type slice goes into the critic's input alongside energy/direction. Adversarial; learns the joint. Under `particle_type.target = "onehot"` it is relaxed through ST-Gumbel first; `"physical"` and `"embedding"` are already continuous and feed the critic directly. | +| `"flow"` / `"ddpm"` | No critic exists -> the type slice trains against its own target, weighted by `particle_type.lambda` (same pattern as `n_sec` today): cross-entropy for `"onehot"`, regression for `"physical"` / `"embedding"`. Non-adversarial. | + +There is therefore **no `particle_type.adversarial` key** — the mechanism follows +from `generator` × `particle_type.target`. + +### 2.2 Rejected alternatives worth remembering + +- **`tie_to_stage1` as a tri-state** (`none`/`gate`/`full`). Sharing experts + between stages is impossible — the stage-1 trunk's input is the 9D target + vector, stage 2's is a token vector of a different width. Sharing the *gate* + is the only meaningful tying, so the key is a bool. +- **Generic `--set path.to.key=value` CLI overrides.** Considered and rejected in + favour of explicit flags (decision 7). +- **Per-expert trunk sizing** (`expert_hidden_dim` / `expert_n_blocks`). Removed + in v0.3.0: experts always use the stage's own `hidden_dim` / `n_res_blocks`. + This was already the effective behaviour — v0.2's `0` sentinel meant "inherit" + and nothing ever set it otherwise. Consequence worth knowing: a routed model is + `n_experts` × the parameters of the monolith at **equal per-row eval cost** + (top-1 dispatch runs one full-size trunk), so routing buys specialization, not + a per-call speedup. + +### 2.3 Correction to an earlier claim + +An earlier draft of this design asserted that a one-hot conditioning mode is +"mathematically identical to an `nn.Embedding` lookup" and should be dropped. +**That is wrong for the mode specified in §3.1.** One-hot here is a *fixed, +unlearned* vector of width `emb_dim` covering the top `emb_dim - 1` species by +training-set count plus an "other" bin. The difference from `embedding` is the +**vocabulary cap**, not the parameterization: with 237 PDG codes and +`emb_dim = 16`, `embedding` gives 237 distinct learned vectors while `onehot` +gives 16 classes. That is a real capacity difference and a real statement about +how rare species are treated, so all three modes are kept. + +--- + +## 3. Config format reference + +Six top-level blocks: `[conditioning]`, `[stage1_model]`, `[stage2_model]`, +`[train]`, plus per-stage sub-tables and the existing `[meta]` (written by +`save_config`, never hand-authored). + +### 3.1 `[conditioning]` + +One block shared by both stages. Each stage still builds its own encoder +*instance* (separate weights) unless `share_stages = true`. + +**The particle and material axes are configured independently and may mix +freely** — e.g. material `physical` with particle `embedding` is a valid and +intended combination. + +```toml +[conditioning] +out_dim = 128 +share_stages = false + +[conditioning.particle] +type = "physical" +emb_dim = 16 +n_layers = 1 + +[conditioning.material] +type = "physical" +emb_dim = 16 +n_layers = 1 +``` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `out_dim` | int | `128` | Width of the fused conditioning vector produced by the encoder's fusion MLP, consumed by every downstream trunk. **New in v0.3.0** — v0.2 hardcoded this as `cond_out_dim = 128` in every constructor signature, unreachable from config. | +| `share_stages` | bool | `false` | `false`: stage 1 and stage 2 each construct their own `ConditionEncoder` with identical config but independent weights (v0.2 behaviour). `true`: one instance, shared by reference. Shared weights halve the conditioning parameter count and force a common representation; independent weights let each stage specialize its view of the pre-step state. | + +#### `[conditioning.particle]` and `[conditioning.material]` + +Identical key sets, applied to the two identity axes independently. + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `type` | `"physical"` \| `"embedding"` \| `"onehot"` | `"physical"` | How this axis's identity becomes an `emb_dim`-wide vector. See the table below. | +| `emb_dim` | int | `16` | Width of this axis's vector. Under `"onehot"` it **also sets the class count** — see below. | +| `n_layers` | int | `1` | Depth of the sub-MLP under `"physical"`. `1` is the single-layer net with `emb_dim` neurons. Ignored under `"embedding"` / `"onehot"`. | + +**The three modes:** + +| mode | particle input | material input | width | learned parameters | +|------|----------------|----------------|-------|--------------------| +| `"physical"` | `log(mass)`, `charge` (`PARTICLE_PHYS_DIM = 2`) | `Z_eff`, `A_eff`, `log(density)`, `log(X0)`, `log(λ_int)` (`MATERIAL_PHYS_DIM = 5`) | `emb_dim` | one `n_layers`-deep MLP with `emb_dim` neurons | +| `"embedding"` | dense vocab index | dense vocab index | `emb_dim` | `nn.Embedding(vocab, emb_dim)` | +| `"onehot"` | top `emb_dim - 1` PDG codes by training-set count, plus one "other" bin | top `emb_dim - 1` materials by count, plus "other" | `emb_dim` | **none** — a fixed vector | + +- `"physical"` reads columns already present in `cond_cont[:, COND_DIM_BASE:]` + (see `giant.data.transforms.build_features`). It is computable for **any** PDG + code or material, which is what allows generalization beyond the training menu. +- `"embedding"` memorizes the training menu — the generalization-comparison + baseline, and the only mode that supports + `stage2_model.particle_type.target = "embedding"` (§3.3). +- `"onehot"` is a *fixed, unlearned* representation. It is **not** a + reparameterization of `"embedding"`: the difference is the vocabulary cap. With + 237 PDG codes and `emb_dim = 16`, `"embedding"` gives 237 distinct learned + vectors while `"onehot"` gives 16 classes. Needs the same data-derived top-N + map as the stage-2 type target (§8). + +**Interaction:** `particle.type = "physical"` is incompatible with router types +`"pdg"` and `"process"`, which build their own dataset-scoped +`nn.Embedding(pdg_vocab, ...)` regardless of the trunk's conditioning mode. +Pairing them silently reintroduces a training-menu-scoped lookup at the routing +layer, defeating the point of physical conditioning. Rejected loudly at build time +— `_check_router_conditioning_compat` in `network.py`, which carries over but must +now read `conditioning.particle.type` rather than a single global mode. + +### 3.2 `[stage1_model]` + +Stage 1 predicts the 9D primary post-step vector +(`giant/constants.py:LOCAL_TARGET_NAMES`). As of decision 1 it carries **no** +`n_sec` head. + +```toml +[stage1_model] +active = true +generator = "flow" +hidden_dim = 256 +n_res_blocks = 6 +dropout = 0.1 +lambda = 1.0 +``` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `active` | bool | `true` | `false` skips building and training stage 1 entirely. The resulting checkpoint holds only stage 2 and **cannot be rolled out** — `giant rollout` must refuse it with a clear error. Used for the meeting's Stage-2-only architecture comparison. | +| `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"flow"` | The generative objective. `"flow"`: conditional flow matching (Lipman et al. 2022), ~10 ODE steps at inference. `"ddpm"`: cosine-schedule diffusion baseline. `"wgan"`: WGAN-GP, single forward pass at inference. Replaces the global `train.mode`. Objective-specific knobs live in the matching sub-table below. | +| `hidden_dim` | int | `256` | Trunk width — also the width of **every expert** under a routed trunk. | +| `n_res_blocks` | int | `6` | Number of `ResBlock`s in the trunk, and in every expert under a routed trunk. Was `model.n_blocks`; renamed for clarity since v0.3.0 also has attention layers in stage 2. | +| `dropout` | float | `0.1` | Dropout inside each `ResBlock`. | +| `lambda` | float | `1.0` | Weight of this stage's loss in the total. Meaningful when both stages are active and non-adversarial; a WGAN stage's adversarial loss drives its own optimizer, so `lambda` scales only its non-adversarial auxiliary terms. | + +#### `[stage1_model.flow]` — read only when `generator = "flow"` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `time_dim` | int | `64` | Width of the `SinusoidalEmbedding` for the flow time variable, concatenated into the trunk's conditioning. **New in v0.3.0** — v0.2 hardcoded 64. | + +#### `[stage1_model.ddpm]` — read only when `generator = "ddpm"` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `time_dim` | int | `64` | As above, for the diffusion time variable. | +| `n_steps` | int | `1000` | Cosine-schedule diffusion steps. **New in v0.3.0** — v0.2 hardcoded this in `CosineSchedule`. | + +#### `[stage1_model.wgan]` — read only when `generator = "wgan"` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `noise_dim` | int | `64` | Width of the generator's input noise vector. There is no time variable, hence no `time_dim`. | +| `n_critic` | int | `5` | Critic updates per generator update (Gulrajani et al. 2017). | +| `gp_weight` | float | `10.0` | Gradient-penalty coefficient. | +| `critic_lr` | float | `0.0` | Critic learning rate. `0.0` means "inherit `train.lr`" — not `None`, since the TOML writer has no null literal to round-trip. | +| `critic_hidden_dim` | int | `0` | Critic trunk width. `0` = inherit `stage1_model.hidden_dim`. **New in v0.3.0** — v0.2 always sized the critic from the generator. | +| `critic_n_res_blocks` | int | `0` | Critic trunk depth. `0` = inherit `stage1_model.n_res_blocks`. | + +#### `[stage1_model.router]` + +Content carries over from v0.2's `[model.router]` unchanged. Reproduced here in +full because the block is now per-stage and its semantics are easy to lose. + +```toml +[stage1_model.router] +enabled = false +type = "energy" +n_experts = 4 +temperature = 0.5 +learn_centers = true +learn_width = false +learn_temperature = false +width_min_ratio = 0.1 +width_max_ratio = 10.0 +lambda_balance = 0.0 +lambda_entropy = 0.0 +lambda_proc = 0.0 +gumbel = false +gumbel_tau_start = 1.0 +gumbel_tau_end = 0.1 +emb_dim = 8 +hidden_dim = 64 +``` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `enabled` | bool | `false` | Replace the monolithic trunk with a mixture of per-expert trunks: soft-mixed over all experts at train time, **top-1 dispatched at eval time** (each row runs exactly one expert). Every expert is `hidden_dim` × `n_res_blocks` — v0.3.0 removes the per-expert sizing keys, so a routed model costs `n_experts` × the monolith's parameters at equal per-row eval cost. Routing buys specialization, not a per-call speedup. | +| `type` | str | `"energy"` | Router impl from `ROUTER_REGISTRY`. `"energy"`: soft turn-on gate over normalized pre-step log-energy. `"pdg"`: gate over a learned PDG embedding. `"process"`: own classifier over pre-step conditioning predicting the step-ending physics process. `"composed"`: joint outer-product gating over multiple axes via `axis{i}_{field}` keys. | +| `n_experts` | int | `4` | Number of experts. For `type = "process"` this doubles as the number of process classes. Ignored for `type = "composed"` (each axis has its own). | +| `temperature` | float | `0.5` | Softmax denominator for the distance-based gate. As `tau -> 0` the gate hardens to nearest-center (Voronoi) selection, which is exactly what eval-time `top1` uses. Energy/pdg routers only. | +| `learn_centers` | bool | `true` | Whether gate centers are `nn.Parameter` or a fixed buffer. | +| `learn_width` | bool | `false` | Give each expert its own learnable width, sigmoid-bounded to `[width_min_ratio, width_max_ratio] * temperature`. Mutually exclusive with `learn_temperature`. | +| `learn_temperature` | bool | `false` | Make the single shared `temperature` learnable, same bounding. Mutually exclusive with `learn_width`. | +| `width_min_ratio` | float | `0.1` | Lower bound multiplier for the above. Must bracket 1.0 with `width_max_ratio` so enabling either mode is a no-op at init. | +| `width_max_ratio` | float | `10.0` | Upper bound multiplier. **Deliberately bounded rather than `softplus`/`exp`:** an unbounded width lets one expert's logit `-d²/width -> 0` almost everywhere, so it wins nearly every row regardless of distance — the same "experts overlap instead of partitioning" failure the router design exists to avoid. | +| `lambda_balance` | float | `0.0` | Importance-CV² load-balancing auxiliary loss weight (Shazeer et al. 2017). **The 2026-07-22 benchmark failure ran with `0.0`; do not repeat that.** | +| `lambda_entropy` | float | `0.0` | Entropy-regularization weight penalizing uniform/collapsed gating. Secondary guard against all experts' widths co-inflating together, which `lambda_balance` cannot see (usage shares stay even throughout that failure). Use with caution: indiscriminate entropy penalties also suppress legitimate ambiguity near a decision boundary. | +| `lambda_proc` | float | `0.0` | Supervised process-classification CE weight. `"process"` router only; `0.0` still trains a working router (the gate gets gradient through the downstream loss) but only `> 0` grounds it in the true `process` label. | +| `gumbel` | bool | `false` | Straight-through Gumbel-softmax train-time combine weights: the forward pass samples a hard one-hot combination (matching eval-time top-1 dispatch exactly) while the backward pass still flows smooth gradient to every expert. Targets the train/eval mismatch. | +| `gumbel_tau_start` | float | `1.0` | Gumbel temperature at step 0, annealed linearly over training. | +| `gumbel_tau_end` | float | `0.1` | Gumbel temperature at the final step. | +| `emb_dim` | int | `8` | The router's **own** pdg (and material) embedding width, separate from the trunk's `ConditionEncoder`. `"pdg"` / `"process"` routers only. | +| `hidden_dim` | int | `64` | The `"process"` router's internal classifier hidden width. | + +**Composed routers** use flat `axis{i}_{field}` keys instead of `type`/`n_experts` +— e.g. `axis0_type = "energy"`, `axis0_n_experts = 4`, `axis1_type = "pdg"`, +`axis1_n_experts = 3`, `axis1_emb_dim = 8`. Indices must be contiguous from 0. +Flat keys keep the block a table of scalars, which the merge machinery relies on. + +**`centers_init`** is not authored by hand: `giant/pipeline.py` populates it for +`type = "energy"` from real data quantiles collected during the existing +normalizer-fitting pass, then writes it into the checkpoint's `model_config`. + +### 3.3 `[stage2_model]` + +Stage 2 predicts `n_sec` and the per-secondary energy/direction/type. + +```toml +[stage2_model] +active = true +decoder = "autoregressive" +generator = "wgan" +hidden_dim = 256 +n_res_blocks = 6 +dropout = 0.1 +lambda = 1.0 +k_max = 15 +context_dim = 64 +stage1_context = "truth" +``` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `active` | bool | `true` | `false` trains stage 1 alone. The checkpoint then has no secondary decoder; `giant rollout` must refuse it, `giant predict` still works. | +| `decoder` | `"one_shot"` \| `"autoregressive"` | `"autoregressive"` | `"one_shot"`: predict all `k_max` slots simultaneously with padded slots masked from the loss — v0.2 behaviour, kept as the baseline arm of the meeting's §7 comparison. `"autoregressive"`: emit one secondary at a time in descending-energy order. | +| `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"wgan"` | As stage 1. Under `"autoregressive"` this is the objective for **each token**: a WGAN token costs one forward pass, a flow token costs ~10 ODE steps. See the cost note in §6.4. | +| `hidden_dim` | int | `256` | Trunk width. | +| `n_res_blocks` | int | `6` | Trunk depth. | +| `dropout` | float | `0.1` | Dropout inside each `ResBlock`. | +| `lambda` | float | `1.0` | Weight of stage 2's loss in the total. Was `train.lambda_s2`. | +| `k_max` | int | `15` | Maximum secondary slots. Under `"one_shot"` this is the fixed output width; under `"autoregressive"` it is a safety cap on the generation loop. Was the global constant `K_MAX` in `giant/constants.py` (max observed `n_sec` is 14 in the PbWO4 dataset, so 15 covers it with one spare). | +| `context_dim` | int | `64` | Width of the projected stage-1 outcome fed into stage 2's conditioning. Was the hardcoded `stage1_proj_dim = 64`. | +| `stage1_context` | `"truth"` \| `"sampled"` | `"truth"` | What stage 2 conditions on during training. `"truth"`: the ground-truth stage-1 target vector, detached — v0.2 behaviour (`train.py:254` passes `x1_s1.detach()`), i.e. stage-level teacher forcing. `"sampled"`: stage 1's own sampled output, closing the train/inference gap at the cost of a sampling pass per batch and a moving target early in training. | + +#### `[stage2_model.n_sec]` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `mode` | `"head"` \| `"stop_token"` \| `"truth"` | `"head"` | `"head"`: a classifier over `{0..k_max}` on the condition encoding alone (no diffusion noise), so it is callable independently at inference — v0.2 behaviour, and what the meeting's §3 explicitly decided to keep. `"stop_token"`: an EOS-style implicit stop, documented in §3 as a later possibility, not a decision. `"truth"`: take `n_sec` from ground truth — only valid for standalone stage-2 evaluation, never for rollout. | +| `lambda` | float | `0.1` | Cross-entropy weight for the head. Was `train.lambda_nsec`. | + +#### `[stage2_model.particle_type]` + +**The three targets mirror the three conditioning modes of §3.1**, and use the +same names. + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `target` | `"onehot"` \| `"physical"` \| `"embedding"` | `"onehot"` | What the token's type slice *is*. See the table below. | +| `lambda` | float | `1.0` | Loss weight. Under `generator = "flow"`/`"ddpm"` this weights the cross-entropy (`"onehot"`) or regression (`"physical"`/`"embedding"`) term; under `"wgan"` the type is adversarial (§2.1) and this weights only any auxiliary term. | +| `other_policy` | `"sample"` \| `"modal"` \| `"drop"` | `"sample"` | How a predicted "other" class becomes a concrete PDG code at rollout, needed because a secondary's mass/charge feed its own downstream conditioning. `"sample"`: draw from the empirical within-bucket distribution recorded at map-build time. `"modal"`: always the most common member. `"drop"`: discard the secondary. Read only under `target = "onehot"`. **Not decided in the meeting** — see §11. | + +**There is no `n_classes` key.** The class count under `target = "onehot"` is +`conditioning.particle.emb_dim` — the same number that sizes the particle axis +everywhere else. One knob sets the model's particle-type resolution, and the +stage-2 onehot classes are by construction the same classes the conditioning +onehot uses, so an emitted secondary's type is directly consumable as the +conditioning of its own next step with no re-mapping. + +Note the coupling this creates: under `conditioning.particle.type = "physical"`, +`emb_dim` primarily means "sub-MLP output width", yet it still sets the stage-2 +class count. Intentional, but worth knowing when tuning either. + +| target | token type slice | width | training target | inverse map at rollout | +|--------|------------------|-------|-----------------|------------------------| +| `"physical"` | regressed `(log mass, charge)` | `2` | the true PDG's physics values (`giant.particles.particle_mass_charge`) | none needed — mass/charge are used directly; `giant.particles.nearest_known_pdg` gives a reporting-only label. v0.2 behaviour. | +| `"onehot"` | class logits | `conditioning.particle.emb_dim` | true class index | `argmax` -> class -> PDG (via `other_policy` for the "other" bin) | +| `"embedding"` | an `emb_dim`-wide vector | `conditioning.particle.emb_dim` | `emb.weight[class].detach()` | L1-nearest row of `emb.weight` — see below | + +So the type slice is `conditioning.particle.emb_dim` wide for **both** `"onehot"` +and `"embedding"`, and `2` only for `"physical"`. + +#### `target = "embedding"` in detail + +Stage 2 emits a vector that should equal **the conditioning's own particle +embedding** for the secondary's species — the same `nn.Embedding` table +`[conditioning.particle]` builds, not a second one. + +**Requires `conditioning.particle.type = "embedding"`.** There is no table to +match against under `"physical"` or `"onehot"`; reject at config-validation time +with an explicit error. + +**Why detached:** the regression target is `emb.weight[class].detach()`, so the +embedding table receives gradient **only through the conditioning path**, never +through the stage-2 output loss. Without the detach the target moves as the +decoder chases it — the exact failure mode that motivated abandoning the learned +type target in the first place (see `decisions/physical-property-conditioning`). +The detach is what makes this option viable again. + +**Inverse map.** The natural exact-match form + +```python +((out - emb.weight).abs().sum(1) < 1e-6).nonzero() +``` + +is correct as a **round-trip assertion in tests** (encode a known PDG, decode, +recover the same PDG) but **must not be used at inference**: a generative model's +continuous output essentially never lands within `1e-6` of a table row, so it +returns an empty tensor almost always. Inference needs the nearest row: + +```python +pdg_idx = (out.unsqueeze(-2) - emb.weight).abs().sum(-1).argmin(-1) # L1 nearest +``` + +Note this decode is **unbounded in vocabulary**, unlike `"onehot"` — every PDG +code in the training vocab is reachable, and there is no "other" bucket, hence no +`other_policy`. The trade-off is that nearest-neighbour decode has no notion of +confidence: an output far from every row still snaps to something. + +#### `[stage2_model.autoregressive]` — read only when `decoder = "autoregressive"` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `order` | `"energy_desc"` | `"energy_desc"` | Canonical generation order. Descending energy is the ordering already flagged as natural in the Phase-2 note's open questions, and the one the existing stick-breaking encoding assumes. Single-valued for now; the key exists so an alternative ordering is not a config break. | +| `history` | `"markov"` \| `"attention"` | `"markov"` | How token *i+1* sees tokens ≤ *i*. `"markov"`: previous token plus running scalars (remaining energy budget, slot index) — a fixed-width summary. `"attention"`: causal self-attention over all emitted tokens. See §6.2 for the trade-off. | +| `teacher_forcing` | `"always"` \| `"scheduled"` \| `"never"` | `"always"` | `"always"`: condition on the ground-truth previous secondary throughout training (the meeting's confirmed plan). `"scheduled"`: scheduled sampling — interpolate toward conditioning on the model's own prediction. `"never"`: free-running from the start. | +| `tf_p_start` | float | `1.0` | Under `"scheduled"`, P(use ground truth) at epoch 0. | +| `tf_p_end` | float | `1.0` | Under `"scheduled"`, P(use ground truth) at the final epoch. Linear interpolation between the two. | +| `attn_n_heads` | int | `4` | Attention heads. Read only under `history = "attention"`. | +| `attn_n_layers` | int | `2` | Causal self-attention layers. Read only under `history = "attention"`. | + +#### `[stage2_model.flow]` / `[stage2_model.ddpm]` + +Same keys as their `[stage1_model.*]` counterparts (`time_dim`; plus `n_steps` +for ddpm). + +#### `[stage2_model.wgan]` — read only when `generator = "wgan"` + +Same keys as `[stage1_model.wgan]` (`noise_dim`, `n_critic`, `gp_weight`, +`critic_lr`, `critic_hidden_dim`, `critic_n_res_blocks`), plus: + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `gumbel_tau_start` | float | `1.0` | Straight-through Gumbel temperature for the **particle-type one-hot** at step 0, annealed linearly. Read only under `particle_type.target = "onehot"` (the other two targets are continuous and need no relaxation). Distinct from `router.gumbel_tau_start`, which anneals expert-combination weights — two unrelated Gumbel relaxations that must not share a key. | +| `gumbel_tau_end` | float | `0.1` | Same, at the final step. | + +Under `"autoregressive"` a fresh `noise_dim` draw is made **per token**. + +#### `[stage2_model.router]` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `tie_to_stage1` | bool | `false` | `true`: stage 2 shares **stage 1's `Router` module instance**, so expert *i* in stage 1 and expert *i* in stage 2 gate on identical conditions by construction. Every other key in this block is then ignored. `false`: an independent router — note this is v0.2's actual behaviour, which built *two separate routers from one config*, so stage-1 expert *i* and stage-2 expert *i* had no semantic relationship despite identical hyperparameters. | + +All other keys are as `[stage1_model.router]`. Invalid when `stage1_model.active += false` and `tie_to_stage1 = true` — reject at config-validation time. + +### 3.4 `[train]` + +Optimizer, schedule, data split and logging only. Everything model-shaped moved +into the stage blocks. + +```toml +[train] +epochs = 100 +batch_size = 4096 +lr = 3e-4 +weight_decay = 0.01 +ema_decay = 0.9999 +warmup_epochs = 5 +val_fraction = 0.1 +max_val_batches = 200 +num_workers = 4 +seed = 0 +validate_every = 10 +validate_steps = 10 +wandb = false +wandb_project = "giant" +wandb_run_name = "" +wandb_log_every = 50 +``` + +| Key | Type | Default | Meaning | +|-----|------|---------|---------| +| `epochs` | int | `100` | Training epochs. | +| `batch_size` | int | `4096` | Steps per batch. `auto` on the CLI estimates from free VRAM. | +| `lr` | float | `3e-4` | AdamW learning rate for every stage's generator. | +| `weight_decay` | float | `0.01` | AdamW weight decay. | +| `ema_decay` | float | `0.9999` | EMA of model weights used for sampling; `0` disables. Maintained per stage. | +| `warmup_epochs` | int | `5` | Linear LR warmup. | +| `val_fraction` | float | `0.1` | Fraction of **events** (not steps) held out — the split is by `event_id` to avoid leaking correlated steps from the same shower. | +| `max_val_batches` | int | `200` | Cap on the per-epoch val-loss pass; `0` = full val set. Distinct from `validate_every`'s marginal/KL pass. | +| `num_workers` | int | `4` | DataLoader workers. Warns above ~1/4 of the machine's CPUs — portal machines are shared. | +| `seed` | int | `0` | Seeds Python/numpy/torch and the event split. | +| `validate_every` | int | `10` | Epochs between full marginal/KL validation passes. | +| `validate_steps` | int | `10` | Sampler steps used during those passes. | +| `wandb` | bool | `false` | Opt-in W&B logging. | +| `wandb_project` | str | `"giant"` | W&B project. | +| `wandb_run_name` | str | `""` | `""` means "use the checkpoint out_dir name" — not `None`, since the TOML writer has no null literal. | +| `wandb_log_every` | int | `50` | Optimizer steps between batch-granularity metric logs. A single epoch can be tens of thousands of steps; per-epoch metrics always log in full. | + +### 3.5 Removed from v0.2 + +| Key | Fate | +|-----|------| +| `train.mode` | Split into `stage1_model.generator` / `stage2_model.generator`. | +| `train.lambda_nsec` | -> `stage2_model.n_sec.lambda`. | +| `train.lambda_s2` | -> `stage2_model.lambda`. | +| `train.n_critic`, `gp_weight`, `critic_lr` | -> `stage{1,2}_model.wgan.*`. | +| `[model]` (whole block) | Split across `[conditioning]` and the two stage blocks. | + +--- + +## 4. Migration: v0.2 -> v0.3 + +`[meta] config_version = 3` tags the new format; **absent means v0.2**. +`migrate_config(cfg) -> cfg` applies the table below and is called on both +`config.toml` load and checkpoint `model_config` load, so nothing on `/ceph` goes +dead (decision 3). + +| v0.2 | v0.3 | +|------|------| +| `train.mode` | `stage1_model.generator` **and** `stage2_model.generator` (same value) | +| `train.lambda_nsec` | `stage2_model.n_sec.lambda` | +| `train.lambda_s2` | `stage2_model.lambda` | +| `train.n_critic` / `gp_weight` / `critic_lr` | `stage1_model.wgan.*` **and** `stage2_model.wgan.*` | +| `model.hidden_dim` / `n_blocks` / `dropout` | `stage{1,2}_model.hidden_dim` / `n_res_blocks` / `dropout` | +| `model.emb_dim` | `conditioning.particle.emb_dim` **and** `conditioning.material.emb_dim` (v0.2 had one shared value) | +| `model.conditioning` | `conditioning.particle.type` **and** `conditioning.material.type` (v0.2 had one shared mode) | +| `model.noise_dim` | `stage{1,2}_model.wgan.noise_dim` | +| `model.router.*` | `stage1_model.router.*`, copied verbatim to `stage2_model.router.*` with `tie_to_stage1 = false` (preserves v0.2's two-independent-routers behaviour) | +| `model.expert_hidden_dim` / `expert_n_blocks` | **dropped.** v0.2's `0` sentinel meant "inherit from the monolith", which is now unconditional. A v0.2 config with a *non-zero* value must fail loudly rather than silently resize the experts — see §4.3. | +| `model.k_max` | `stage2_model.k_max` | +| — | `conditioning.out_dim = 128` (v0.2's hardcoded value) | +| — | `conditioning.{particle,material}.n_layers = 2` (v0.2's hardcoded depth; note the v0.3 *default* is 1) | +| — | `stage{1,2}_model.flow.time_dim = 64` / `.ddpm.time_dim = 64` (v0.2's hardcoded value) | +| — | `stage2_model.context_dim = 64` (v0.2's hardcoded `stage1_proj_dim`) | +| — | `stage2_model.decoder = "one_shot"` (v0.2 had no other option) | +| — | `stage2_model.particle_type.target = "physical"` (v0.2 behaviour) | +| — | `stage{1,2}_model.active = true` | + +### 4.1 The awkward one: `n_sec` head ownership + +Decision 1 moves the `n_sec` head to stage 2, but **v0.2 checkpoints carry +`n_sec_head` weights inside the stage-1 module** (`DenoisingMLP.n_sec_head`, +`WGANGenerator.n_sec_head`, `RoutedDenoisingMLP.n_sec_head`). The shim must keep +those loading where they are. + +Handling: `migrate_config` sets an internal +`stage2_model.n_sec.legacy_owner = "stage1"` that `build_models` honours by +attaching the head to the stage-1 module. Never written by new runs, never +CLI-settable, never documented as a user-facing option. + +### 4.2 Non-inheriting expert dims + +v0.3.0 drops `expert_hidden_dim` / `expert_n_blocks` (§2.2). Migration must +distinguish two cases: + +- value is `0` (the "inherit" sentinel, and what every real run used) — drop the + key silently, behaviour is unchanged. +- value is non-zero — **fail loudly.** Silently resizing those experts to + `hidden_dim` would change the architecture, so the checkpoint's weights would no + longer match. Such a checkpoint can only be loaded by v0.2. + +### 4.3 Migration test + +The acceptance criterion for the whole shim: **load a v0.2 checkpoint through +`migrate_config` + the new `build_models`, and diff its outputs against v0.2 code +on the same input batch.** Bit-identical, or the refactor has changed something it +should not have. Pick one flow checkpoint and one WGAN checkpoint from `/ceph`. + +--- + +## 5. network.py refactor + +### 5.1 What it looks like today + +Ten classes that are permutations of three independent choices: + +| | flow/ddpm | wgan generator | wgan critic | +|---|---|---|---| +| **stage 1** | `DenoisingMLP` | `WGANGenerator` | `Critic` | +| **stage 1, routed** | `RoutedDenoisingMLP` | — | — | +| **stage 2** | `SecondaryDecoder` | `WGANSecondaryGenerator` | `SecondaryCritic` | +| **stage 2, routed** | `RoutedSecondaryDecoder` | — | — | + +The empty cells are the entire reason `giant/pipeline.py:275` hard-rejects +`--mode wgan --router`: no routed WGAN generator class was ever written. There is +no deeper reason — the routed trunk is orthogonal to the objective. + +Every one of those classes repeats the same body: build a condition encoder, +optionally a time embedding, project input, run blocks, project output. + +### 5.2 Proposed decomposition — one axis per config block + +**(a) `[conditioning]` -> encoders** + +```python +ConditionEncoder(type, emb_dim, n_layers, out_dim) # behaviour unchanged, now configurable +ContextAdapter(in_dim, context_dim) # stage-1 outcome -> context vector +``` + +`SecondaryConditionEncoder` **disappears as a class**. It was +`ConditionEncoder` + a `stage1_proj` linear + a fuse layer; those compose at the +stage-model level instead. + +**(b) `[stage*_model]` + `.router` -> trunks, behind one interface** + +```python +class Trunk(nn.Module): + def forward(self, x, cond, cond_cont=None, cond_cat=None) -> Tensor: ... + +MonolithicTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) +RoutedTrunk(router, in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) + +build_trunk(stage_cfg, in_dim, out_dim, cond_dim) -> Trunk +``` + +`ExpertTrunk` and `_route_forward` carry over unchanged. **One required change:** +`MonolithicTrunk` and `ExpertTrunk` need a separate `out_dim` — today +`ExpertTrunk` hardcodes `out_proj = nn.Linear(hidden_dim, in_dim)`, i.e. +`out_dim == in_dim`. That stops working the moment stage 2's per-token output is +`4 + type_dim` wide while its input is a noise vector of width `noise_dim`. + +**(c) `[stage*_model].generator` -> a thin wrapper, not a class family** + +The generator choice controls exactly two things: + +- whether `SinusoidalEmbedding(t)` is concatenated into `cond` (flow/ddpm) or not (wgan) +- whether the trunk's `x` is the diffused/interpolated `x_t` (flow/ddpm) or a noise draw `z` (wgan) + +That is small enough for one wrapper, collapsing six of today's ten classes: + +```python +class GenerativeTrunk(nn.Module): + """cond-encode -> (optional time-embed) -> trunk. Backs flow, ddpm and wgan.""" + def forward(self, x, cond_cont, cond_cat, t=None, context=None) -> Tensor: ... +``` + +### 5.3 Resulting class list + +``` +# building blocks +SinusoidalEmbedding, ResBlock, ConditionEncoder, ContextAdapter + +# trunks +Trunk (ABC), MonolithicTrunk, RoutedTrunk, ExpertTrunk + +# routers — carried over unchanged +Router, EnergyRouter, PdgRouter, ProcessRouter, ComposedRouter +ROUTER_REGISTRY, register_router, build_router, build_composed_router + +# history encoders — new, stage-2 AR only +HistoryEncoder (ABC), MarkovHistory, AttentionHistory + +# stage models +Stage1Model # 9D primary step +Stage2OneShot # k_max slots at once (v0.2 behaviour) +Stage2Autoregressive # one token at a time +CriticModel # stage-1 or stage-2 critic, generator-agnostic + +# factories +build_models(cfg) -> {"stage1": ... | None, "stage2": ... | None} +build_critics(cfg) -> {"stage1": ... | None, "stage2": ... | None} +``` + +Ten classes become four stage classes plus reusable parts, and **routed WGAN comes +for free** — `pipeline.py`'s rejection can be deleted. + +### 5.4 Factory signature change + +`build_models` returns a **dict, not a tuple**: `active = false` on either stage +means that key is `None`. Every caller unpacking +`stage1, sec_decoder = build_models(...)` must be updated +(`pipeline.py:390`, `cli.py:871`, `cli.py:1254`). + +--- + +## 6. Stage-2 autoregressive design + +### 6.1 Token layout + +Secondaries are emitted one at a time in descending energy order. Per-token output +width is `4 + type_dim`: + +| slice | meaning | +|-------|---------| +| `[0]` | stick-breaking logit — fraction of the **remaining** energy budget | +| `[1:4]` | local-frame direction, normalized to a unit vector | +| `[4:4+type_dim]` | particle type — see below | + +`type_dim` follows `particle_type.target` (§3.3): `2` for `"physical"`, and +`conditioning.particle.emb_dim` for both `"onehot"` and `"embedding"`. +Only `"onehot"` involves a relaxation — ST-Gumbel under `wgan`, cross-entropy +under `flow`/`ddpm`; the other two are continuous and feed the critic (or the +regression loss) directly. + +Per-token conditioning is: + +``` +base condition encoding (ConditionEncoder, [conditioning].out_dim) ++ stage-1 context (ContextAdapter, stage2_model.context_dim) ++ history_encoder(prefix) (§6.2) ++ running scalars (remaining energy budget, slot index) +``` + +### 6.2 History encoders + +One interface, `history_encoder(prefix) -> fixed-width vector`: + +- **`MarkovHistory`** — the previous token's `(energy_fraction, direction, + type_embedding)`. Fixed-width, one small MLP. +- **`AttentionHistory`** — causal self-attention over all emitted tokens, taking + the last position. `attn_n_heads` × `attn_n_layers`. + +**The trade-off** (this is decision 8's reasoning, recorded so it does not have to +be re-derived): + +1. **Markov is less impoverished than it sounds.** The remaining-energy budget is + an *exact sufficient statistic* for the conservation constraint — stick-breaking + needs nothing else from history. Slot index likewise. What markov genuinely + cannot see is set *composition*: "I have already emitted two photons and an + electron." That matters for correlated production — pair production emits + exactly e⁺e⁻, a brems cascade correlates species across the set. Note that the + meeting's charge-conservation idea was exactly such a hand-engineered summary + statistic, and it is out of scope for v0.3.0 (decision 6), so markov does not + get that crutch. +2. **Sequences are short and front-loaded.** `n_sec` is 0–2 for most steps, max 14. + For K ≤ 2, attention *is* markov. They diverge only in the high-multiplicity + tail — physically interesting but data-poor, so the attention path trains on + few examples where it actually matters. +3. **Cost is not where intuition puts it.** Under teacher forcing both train in a + single parallel pass over all K tokens (every token's input is ground truth, so + nothing is sequential). At inference both need K sequential forwards; attention + additionally needs a KV cache to avoid re-encoding the prefix. Attention's + marginal FLOPs over ≤15 tokens are rounding error. +4. **Exposure bias cuts against attention.** Attention conditions on the entire + generated prefix, so one off-manifold early token poisons every later token + through the context. Markov's fixed summary sees only one bad token, and the + budget scalars stay exact regardless. Given the explicitly-flagged train/ + inference gap and the known compounding-rollout-error problem, the more + expressive history is also the more fragile one. + +Hence: markov is the default and the baseline; attention is a flag. + +### 6.3 Energy budget under AR + +**No re-derivation needed**, despite the meeting's action item suggesting +otherwise. The existing stick-breaking is already sequential in spirit — each slot +takes a fraction of what remains — so it carries over to per-token generation +directly by feeding "remaining budget" as a per-token conditioning scalar. +Conservation stays exact by construction: the valid slots' energies sum to `e_sec`, +which the Stage-1 simplex already guarantees sums correctly with `edep` and +`post_E`. + +### 6.4 Cost warning + +AR costs **K sequential forward passes per step** where one-shot costs one. +Against the ~10× native-Geant4 eval budget that motivated the whole fast-eval +track, this is the number to watch — not the history-encoder choice. With +`generator = "flow"` it is worse still: ~10 ODE steps per token, so ~150 forwards +per step in the worst case. `generator = "wgan"` (one pass per token) is the only +configuration that plausibly meets the budget; flow AR is for quality comparison. + +--- + +## 7. Training loop + +Decision 2 (full mixed per-stage objectives) makes `train.py` one trainer object +per active stage: + +```python +class StageTrainer: # owns optimizers, EMA, update cadence + def step(self, batch, global_step) -> dict[str, float]: ... + +FlowTrainer, DDPMTrainer, WGANTrainer(critic, n_critic, gp_weight) +``` + +- Non-adversarial stages contribute `lambda * loss` to one backward pass. +- A WGAN stage runs its own critic inner loop on the same batch, with a generator + update every `n_critic`-th batch — today's `_wgan_train_step` cadence. +- A mixed run (`flow` + `wgan`) steps stage 1 every batch while stage 2 does 5 + critic updates then a generator update. Independent optimizers, independent EMA. +- Router auxiliary losses (`lambda_balance` / `lambda_proc` / `lambda_entropy`) + become per-stage, summed over whichever stages are routed. Today's + `hasattr(model, "router")` check (`train.py:262`) generalizes cleanly. +- `metrics.csv` and W&B metric names gain a stage prefix. +- With `stage1_model.active = false`, stage 2 still needs its stage-1 context: it + comes from the ground-truth target already in the batch (`x1_s1`), which is + exactly what v0.2 does anyway. **Stage-2-only training is therefore a cheap + ablation, not new plumbing** — drop the stage-1 loss, skip building stage 1. + +--- + +## 8. Data and setup-cache changes + +Three config options need a "top N−1 by training-set count plus other" map, but +they resolve to **at most two distinct maps per run** — one per axis — because +the class count always comes from that axis's `emb_dim`: + +| consumer | axis | N | +|----------|------|---| +| `conditioning.particle.type = "onehot"` | PDG | `conditioning.particle.emb_dim` | +| `stage2_model.particle_type.target = "onehot"` | PDG | `conditioning.particle.emb_dim` | +| `conditioning.material.type = "onehot"` | material | `conditioning.material.emb_dim` | + +The two PDG consumers therefore **share one map** — which is the point of +dropping `n_classes`: a secondary's emitted type is directly consumable as the +conditioning of its own next step, with no re-mapping between two class systems. +Build one shared helper, structurally identical to today's `proc_map`: + +- `build_topn_map_from_files(files, column, n_classes=...)` in + `giant/data/loader.py`, next to `build_process_map_from_files` +- a `setup_cache` section keyed by `(axis, N)`, same shape as `cache.proc_maps` + (which is keyed by `n_experts`). N is still part of the key so the sidecar stays + reusable across runs with different `emb_dim`, even though a single run only + ever needs one N per axis. +- persisted into the checkpoint beside `pdg_map` / `mat_map` +- inverted at rollout to recover a concrete PDG -> mass/charge per secondary + +Record the **empirical within-bucket distribution** at map-build time as well — +`other_policy = "sample"` needs it. + +`particle_type.target = "embedding"` needs no map: it reaches the full training +vocab through the conditioning's embedding table (§3.3). + +`giant/constants.py`: `K_MAX` and `SEC_DIM` stop being authoritative constants +(they become `stage2_model.k_max` and a derived quantity). Keep them as defaults +only, and audit the ~12 modules importing `K_MAX` for places that assume it is +global truth. + +--- + +## 9. Config machinery changes + +All in `giant/config.py`: + +- **`merge_cli_overrides`** — replace the hand-written one-level router merge with + a generic recursive deep-merge. The new layout is three levels deep + (`stage1_model.router.axis0_type`). +- **`save_config`** — recursive TOML writer; today it handles exactly one nesting + level (see its `nested_sections` list). +- **`default_out_dir_name`** — `_OUT_DIR_NAME_CANDIDATES` entries become dotted + paths (`"stage2_model.decoder"`) instead of `(section, field)` pairs. Add + candidates for the new discriminating fields: `decoder`, per-stage `generator`, + `particle_type.target`, `autoregressive.history`. +- **`resolve_expert_dims`** — **deleted.** Experts always take the stage's + `hidden_dim` / `n_res_blocks` (§2.2). Its two callers (`pipeline.py:351` and the + CLI's batch-size auto-estimate) read the stage keys directly, as does + `pipeline.py`'s "experts are NxM, different from model.hidden_dim" warning, + which becomes unreachable and should go. +- **`migrate_config(cfg) -> cfg`** — §4's table, applied on both `config.toml` load + and checkpoint `model_config` load. New `[meta] config_version = 3`. +- **`Conditioning` enum** — gains a third member `onehot`, and now feeds **two** + keys (`conditioning.particle.type`, `conditioning.material.type`) rather than + one. Shared with `scripts/dwarf.py`, so both CLIs stay in sync. +- **Cross-block validation** — a new `validate_config(cfg)` pass, since v0.3.0 has + constraints no single block can check: + `particle_type.target = "embedding"` requires + `conditioning.particle.type = "embedding"`; router types `"pdg"`/`"process"` + require `conditioning.particle.type != "physical"`; + `stage2_model.router.tie_to_stage1` requires `stage1_model.active`; + `n_sec.mode = "truth"` is invalid for a rollout-capable checkpoint. +- **`estimate_batch_size`** — its calibration constants assume the v0.2 + architecture ("post-Phase-2, including the Stage-2 secondary decoder and n_sec + head"). AR stage 2 changes the activation-memory profile; re-measure and note + the new calibration point. + +--- + +## 10. Callers that need updating + +| File | Why | +|------|-----| +| `giant/pipeline.py` | Builds `model_config`; now per-stage. Delete the wgan+router rejection at `:275`. Router `centers_init` seeding becomes per-stage. | +| `giant/train.py` | Per-stage trainers (§7). | +| `giant/cli.py` | Stage-prefixed flags for `train` and `new-run`; `build_models` now returns a dict (`:871`, `:1254`); `:1339` writes `model_config` into the rollout sidecar. | +| `giant/sample.py` | Sampler picked per stage from `stage*_model.generator`; new AR sampling loop with KV cache under `history = "attention"`. | +| `giant/rollout.py` | AR secondary generation; categorical class -> PDG decode; `other_policy` handling. | +| `giant/validate.py` | Stage-2 marginals gain a type-class marginal. | +| `giant/analysis/render.py` | `_router_summary(model_config)` at `:44` reads `model_config["router"]`. | +| `giant/analysis/router_gating.py` | Same, at `:76`. | +| `giant/constants.py` | `K_MAX` / `SEC_DIM` demoted to defaults (§8). | +| `configs/*.toml` | All eight shipped configs are v0.2-format; regenerate or rely on the shim. | +| `condor-gpu-train-rollout` branch | Submits `giant train` flags; needs rebasing onto the new flag surface. | + +--- + +## 11. Open questions + +1. **"Other" bucket at rollout.** A predicted "other" class has no concrete PDG, + so no mass/charge for that secondary's own downstream conditioning. + `other_policy` is the config surface; the mechanism is a judgement call + (`"sample"` from the empirical within-bucket distribution seems least biased). + **Not decided in the meeting.** +2. **Charge conservation.** Out of scope for v0.3.0 (decision 6), but worth + recording *why* it interacts with decision 5: a categorical head makes a hard + mechanism tractable — mask classes whose charge cannot fit the remaining charge + budget before the softmax, analogous to the energy simplex. This is **only** + possible with a categorical type; the continuous `(mass, charge)` target admits + no such mask. That is a second, independent argument for the §5 switch beyond + the species-collapse one. +3. **Does `"stop_token"` deserve building in v0.3.0?** The meeting kept the `n_sec` + head and recorded the implicit stop as a later possibility. The config key + exists; the implementation can be deferred. +4. **`estimate_batch_size` calibration** for AR stage 2 (§9). +5. **Nearest-neighbour decode has no reject option.** Under + `particle_type.target = "embedding"`, an output far from every table row still + snaps to its nearest neighbour — there is no equivalent of `"onehot"`'s "other" + bin, and no confidence signal. Worth logging the L1 distance distribution at + rollout: a heavy tail means the decoder is emitting vectors off the embedding + manifold, which would be the direct analogue of the species-collapse symptom + this redesign is chasing. +6. **Should `flow` and `ddpm` share one sub-table?** Both need `time_dim`; only + ddpm needs `n_steps`. Currently duplicated as `[stage*_model.flow]` and + `[stage*_model.ddpm]`. A single `[stage*_model.diffusion]` would avoid the + duplication at the cost of a name that fits flow matching poorly. +5. **Differentiability sanity check.** The meeting's §5 notes that the original + argument for the continuous type target rested on avoiding a non-differentiable + categorical *sampling* step — but per-token training loss is differentiable + either way under teacher forcing, and full shower-rollout backprop is already + structurally non-differentiable once secondaries spawn branches. The note flags + this as "worth confirming explicitly with Jan rather than assuming it". + +--- + +## 12. Implementation order + +1. **`config.py`** — new `DEFAULT_CONFIG`, recursive merge/write, `migrate_config`, + tests. Nothing else can land first. +2. **`network.py`** — the §5 decomposition, with `Stage2OneShot` reproducing v0.2 + exactly. Gate on the §4.3 migration test: load a v0.2 checkpoint through the + shim and diff outputs against v0.2 code. +3. **`train.py`** — per-stage trainers; `active = false` paths. At this point + Stage-2-only training works and the meeting's step 2 (one-shot WGAN baseline, + trained standalone) is runnable. +4. **Type map** — `loader.py` + `setup_cache.py` + checkpoint persistence + + `particle_type.target = "onehot"` in `Stage2OneShot`. This is the meeting's + action item 1, and it is testable against the one-shot baseline before any AR + work. +5. **`Stage2Autoregressive`** with `history = "markov"`, `teacher_forcing = + "always"`. The meeting's step 3. +6. **`sample.py` / `rollout.py`** — AR generation and class -> PDG decode, so an AR + model can actually be rolled out and put through `giant analyze`. +7. **`history = "attention"`, scheduled sampling** — then run the meeting's §7 + comparison (one-shot vs autoregressive, standalone) and only chain the winner + behind Stage 1. + +Steps 1–3 are pure refactor with a bit-identical acceptance criterion. Steps 4–7 +are the actual physics change. -- 2.39.5 From 376bdb9d0809061de381f4eab3f733045ede89d8 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 09:54:48 +0200 Subject: [PATCH 02/30] Refine v0.3.0 design: defaults, deferred scope, open questions Config defaults: dropout 0.1 -> 0.0, wandb false -> true. The v0.3.0 work is a sequence of architecture comparisons, and an unlogged run is not comparable, so W&B is on unless explicitly disabled. Restructure the open-questions section into settled / deferred / tracked / still-open, since most of it is now decided: - other_policy three-way switch and separate flow/ddpm sub-tables are approved as specified. - stop_token is schema-valid but raises "not implemented in v0.3.0"; charge conservation gets no key at all and is left deliberately undesigned, to be worked out on its own terms rather than pre-shaped by this refactor. The speculative charge-mask sketch is removed. - Logging the L1 decode-distance distribution under target = "embedding" becomes tracked implementation work, landing with the rollout decode. - estimate_batch_size recalibration becomes implementation step 8, last: the activation-memory profile is not knowable until the AR trunk and history encoder are final, so it is measured on real hardware with the example configs rather than guessed. Only the differentiability question for Jan remains genuinely open. Co-Authored-By: Claude Opus 5 --- docs/v0.3.0-design.md | 112 +++++++++++++++++++++++++----------------- 1 file changed, 66 insertions(+), 46 deletions(-) diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md index 5b4408d..f1b35c4 100644 --- a/docs/v0.3.0-design.md +++ b/docs/v0.3.0-design.md @@ -24,7 +24,7 @@ to build it. Read it before touching `giant/config.py` or `giant/model/network.p 8. [Data and setup-cache changes](#8-data-and-setup-cache-changes) 9. [Config machinery changes](#9-config-machinery-changes) 10. [Callers that need updating](#10-callers-that-need-updating) -11. [Open questions](#11-open-questions) +11. [Settled scope and open questions](#11-settled-scope-and-open-questions) 12. [Implementation order](#12-implementation-order) --- @@ -201,7 +201,7 @@ active = true generator = "flow" hidden_dim = 256 n_res_blocks = 6 -dropout = 0.1 +dropout = 0.0 lambda = 1.0 ``` @@ -211,7 +211,7 @@ lambda = 1.0 | `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"flow"` | The generative objective. `"flow"`: conditional flow matching (Lipman et al. 2022), ~10 ODE steps at inference. `"ddpm"`: cosine-schedule diffusion baseline. `"wgan"`: WGAN-GP, single forward pass at inference. Replaces the global `train.mode`. Objective-specific knobs live in the matching sub-table below. | | `hidden_dim` | int | `256` | Trunk width — also the width of **every expert** under a routed trunk. | | `n_res_blocks` | int | `6` | Number of `ResBlock`s in the trunk, and in every expert under a routed trunk. Was `model.n_blocks`; renamed for clarity since v0.3.0 also has attention layers in stage 2. | -| `dropout` | float | `0.1` | Dropout inside each `ResBlock`. | +| `dropout` | float | `0.0` | Dropout inside each `ResBlock`. **Default changed in v0.3.0** (was `0.1`). | | `lambda` | float | `1.0` | Weight of this stage's loss in the total. Meaningful when both stages are active and non-adversarial; a WGAN stage's adversarial loss drives its own optimizer, so `lambda` scales only its non-adversarial auxiliary terms. | #### `[stage1_model.flow]` — read only when `generator = "flow"` @@ -304,7 +304,7 @@ decoder = "autoregressive" generator = "wgan" hidden_dim = 256 n_res_blocks = 6 -dropout = 0.1 +dropout = 0.0 lambda = 1.0 k_max = 15 context_dim = 64 @@ -318,7 +318,7 @@ stage1_context = "truth" | `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"wgan"` | As stage 1. Under `"autoregressive"` this is the objective for **each token**: a WGAN token costs one forward pass, a flow token costs ~10 ODE steps. See the cost note in §6.4. | | `hidden_dim` | int | `256` | Trunk width. | | `n_res_blocks` | int | `6` | Trunk depth. | -| `dropout` | float | `0.1` | Dropout inside each `ResBlock`. | +| `dropout` | float | `0.0` | Dropout inside each `ResBlock`. **Default changed in v0.3.0** (was `0.1`). | | `lambda` | float | `1.0` | Weight of stage 2's loss in the total. Was `train.lambda_s2`. | | `k_max` | int | `15` | Maximum secondary slots. Under `"one_shot"` this is the fixed output width; under `"autoregressive"` it is a safety cap on the generation loop. Was the global constant `K_MAX` in `giant/constants.py` (max observed `n_sec` is 14 in the PbWO4 dataset, so 15 covers it with one spare). | | `context_dim` | int | `64` | Width of the projected stage-1 outcome fed into stage 2's conditioning. Was the hardcoded `stage1_proj_dim = 64`. | @@ -328,7 +328,7 @@ stage1_context = "truth" | Key | Type | Default | Meaning | |-----|------|---------|---------| -| `mode` | `"head"` \| `"stop_token"` \| `"truth"` | `"head"` | `"head"`: a classifier over `{0..k_max}` on the condition encoding alone (no diffusion noise), so it is callable independently at inference — v0.2 behaviour, and what the meeting's §3 explicitly decided to keep. `"stop_token"`: an EOS-style implicit stop, documented in §3 as a later possibility, not a decision. `"truth"`: take `n_sec` from ground truth — only valid for standalone stage-2 evaluation, never for rollout. | +| `mode` | `"head"` \| `"stop_token"` \| `"truth"` | `"head"` | `"head"`: a classifier over `{0..k_max}` on the condition encoding alone (no diffusion noise), so it is callable independently at inference — v0.2 behaviour, and what the meeting's §3 explicitly decided to keep. `"stop_token"`: an EOS-style implicit stop — **accepted by the schema but not implemented in v0.3.0**, raising a clear "not implemented" error if set (§11.2). The key exists now so landing the mechanism later is not a config break. `"truth"`: take `n_sec` from ground truth — only valid for standalone stage-2 evaluation, never for rollout. | | `lambda` | float | `0.1` | Cross-entropy weight for the head. Was `train.lambda_nsec`. | #### `[stage2_model.particle_type]` @@ -456,7 +456,7 @@ num_workers = 4 seed = 0 validate_every = 10 validate_steps = 10 -wandb = false +wandb = true wandb_project = "giant" wandb_run_name = "" wandb_log_every = 50 @@ -476,7 +476,7 @@ wandb_log_every = 50 | `seed` | int | `0` | Seeds Python/numpy/torch and the event split. | | `validate_every` | int | `10` | Epochs between full marginal/KL validation passes. | | `validate_steps` | int | `10` | Sampler steps used during those passes. | -| `wandb` | bool | `false` | Opt-in W&B logging. | +| `wandb` | bool | `true` | W&B per-epoch metric logging. **Default changed in v0.3.0** (was opt-in `false`): the v0.3.0 work is a sequence of architecture comparisons, and a run that was not logged is not comparable. Set `false` for throwaway/debug runs. | | `wandb_project` | str | `"giant"` | W&B project. | | `wandb_run_name` | str | `""` | `""` means "use the checkpoint out_dir name" — not `None`, since the TOML writer has no null literal. | | `wandb_log_every` | int | `50` | Optimizer steps between batch-granularity metric logs. A single epoch can be tens of thousands of steps; per-epoch metrics always log in full. | @@ -845,8 +845,8 @@ All in `giant/config.py`: `n_sec.mode = "truth"` is invalid for a rollout-capable checkpoint. - **`estimate_batch_size`** — its calibration constants assume the v0.2 architecture ("post-Phase-2, including the Stage-2 secondary decoder and n_sec - head"). AR stage 2 changes the activation-memory profile; re-measure and note - the new calibration point. + head"). AR stage 2 changes the activation-memory profile. **Recalibrate last** + (§12 step 8), measured on real hardware with the example configs. --- @@ -868,41 +868,55 @@ All in `giant/config.py`: --- -## 11. Open questions +## 11. Settled scope and open questions -1. **"Other" bucket at rollout.** A predicted "other" class has no concrete PDG, - so no mass/charge for that secondary's own downstream conditioning. - `other_policy` is the config surface; the mechanism is a judgement call - (`"sample"` from the empirical within-bucket distribution seems least biased). - **Not decided in the meeting.** -2. **Charge conservation.** Out of scope for v0.3.0 (decision 6), but worth - recording *why* it interacts with decision 5: a categorical head makes a hard - mechanism tractable — mask classes whose charge cannot fit the remaining charge - budget before the softmax, analogous to the energy simplex. This is **only** - possible with a categorical type; the continuous `(mass, charge)` target admits - no such mask. That is a second, independent argument for the §5 switch beyond - the species-collapse one. -3. **Does `"stop_token"` deserve building in v0.3.0?** The meeting kept the `n_sec` - head and recorded the implicit stop as a later possibility. The config key - exists; the implementation can be deferred. -4. **`estimate_batch_size` calibration** for AR stage 2 (§9). -5. **Nearest-neighbour decode has no reject option.** Under - `particle_type.target = "embedding"`, an output far from every table row still - snaps to its nearest neighbour — there is no equivalent of `"onehot"`'s "other" - bin, and no confidence signal. Worth logging the L1 distance distribution at - rollout: a heavy tail means the decoder is emitting vectors off the embedding - manifold, which would be the direct analogue of the species-collapse symptom - this redesign is chasing. -6. **Should `flow` and `ddpm` share one sub-table?** Both need `time_dim`; only - ddpm needs `n_steps`. Currently duplicated as `[stage*_model.flow]` and - `[stage*_model.ddpm]`. A single `[stage*_model.diffusion]` would avoid the - duplication at the cost of a name that fits flow matching poorly. -5. **Differentiability sanity check.** The meeting's §5 notes that the original - argument for the continuous type target rested on avoiding a non-differentiable - categorical *sampling* step — but per-token training loss is differentiable - either way under teacher forcing, and full shower-rollout backprop is already - structurally non-differentiable once secondaries spawn branches. The note flags - this as "worth confirming explicitly with Jan rather than assuming it". +### 11.1 Settled — build as specified + +- **`other_policy` switch.** The three-way `"sample"` / `"modal"` / `"drop"` + design in §3.3 is approved as the config surface for turning a predicted "other" + class into a concrete PDG at rollout. `"sample"` (draw from the empirical + within-bucket distribution recorded at map-build time) stays the default as the + least-biased option. +- **`[stage*_model.flow]` and `[stage*_model.ddpm]` stay separate tables.** The + duplicated `time_dim` is accepted; a merged `[stage*_model.diffusion]` was + considered and rejected because the name fits flow matching poorly. + +### 11.2 Deferred — not implemented in v0.3.0 + +- **`n_sec.mode = "stop_token"`.** The value is **accepted by the schema** but + raises a clear "not implemented in v0.3.0" error if set. The key existing now + means landing the implicit-stop mechanism later is not a config break. `"head"` + is what v0.3.0 builds, per the meeting's §3 decision. +- **Charge conservation.** No key at all — `[stage2_model.conservation]` is absent + from v0.3.0 (decision 6), unlike `stop_token` above. **Deliberately left + undesigned:** the mechanism should be worked out on its own terms when it is + taken up, not pre-shaped by choices made for this refactor. Adding the block + later is a config addition, not a break. + +### 11.3 Tracked as implementation work + +- **Log the L1 distance distribution at rollout** under + `particle_type.target = "embedding"`. Nearest-neighbour decode has no reject + option — an output far from every table row still snaps to its nearest neighbour, + with no "other" bin and no confidence signal. A heavy tail in that distribution + means the decoder is emitting vectors off the embedding manifold, which is the + direct analogue of the species-collapse symptom this redesign exists to fix. + Belongs with the `giant/rollout.py` decode work (step 6) and should surface as a + `giant analyze` diagnostic plot alongside `router_gating`. +- **`estimate_batch_size` recalibration** for AR stage 2 is the **last** step of + the implementation flow (§12 step 8), measured on real hardware using the example + configs — not guessed from the existing calibration constants. + +### 11.4 Still open + +- **Differentiability sanity check with Jan.** The meeting's §5 notes that the + original argument for the continuous type target rested on avoiding a + non-differentiable categorical *sampling* step — but per-token training loss is + differentiable either way under teacher forcing, and full shower-rollout backprop + is already structurally non-differentiable once secondaries spawn branches. The + note flags this as "worth confirming explicitly with Jan rather than assuming + it". Decision 5 rests on this reasoning, so it is worth raising before step 4 + rather than after. --- @@ -923,10 +937,16 @@ All in `giant/config.py`: 5. **`Stage2Autoregressive`** with `history = "markov"`, `teacher_forcing = "always"`. The meeting's step 3. 6. **`sample.py` / `rollout.py`** — AR generation and class -> PDG decode, so an AR - model can actually be rolled out and put through `giant analyze`. + model can actually be rolled out and put through `giant analyze`. Includes the + L1-distance diagnostic of §11.3. 7. **`history = "attention"`, scheduled sampling** — then run the meeting's §7 comparison (one-shot vs autoregressive, standalone) and only chain the winner behind Stage 1. +8. **`estimate_batch_size` recalibration** — last, once the architectures are + settled. Measure on real hardware with the example configs and record the new + calibration points in `config.py` (§9, §11.3). Steps 1–3 are pure refactor with a bit-identical acceptance criterion. Steps 4–7 -are the actual physics change. +are the actual physics change. Step 8 is measurement, deliberately last: the +activation-memory profile is not knowable until the AR trunk and its history +encoder are final. -- 2.39.5 From a489991a3bf34a647b8d158cdfeeb66c1d631619 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 09:57:44 +0200 Subject: [PATCH 03/30] Document the differentiability position and its validation obligation The categorical type path is not differentiable, and v0.3.0 accepts that: the expected contribution of the broken path to the total gradient is assumed negligible. Record it as an assumption with an explicit obligation to demonstrate it, not as a settled result. Separates the three things "broken" covers, since they have different status: per-token loss under teacher forcing is fine (softmax CE needs no sampling); ST-Gumbel into the critic is biased rather than absent (hard forward, soft backward); full shower-rollout backprop was already structurally non-differentiable once secondaries branch, so the switch costs nothing that was not already lost. The accepted claim concerns only the middle one. Lists three ways to falsify it, cheapest first: gradient-magnitude accounting through the type slice vs the continuous slices, a detached-type ablation, and an estimator swap against REINFORCE if those are inconclusive. The first is wired into implementation step 5 so evidence accrues during the architecture comparison rather than in a dedicated run afterwards, and the fallback if the ratio is not small is a config change (target = "physical" or a non-adversarial CE head), not a redesign. Co-Authored-By: Claude Opus 5 --- docs/v0.3.0-design.md | 58 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 47 insertions(+), 11 deletions(-) diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md index f1b35c4..b5ebe64 100644 --- a/docs/v0.3.0-design.md +++ b/docs/v0.3.0-design.md @@ -67,7 +67,7 @@ only", "stage 1 flow + stage 2 WGAN", or "one-shot vs autoregressive stage 2". | 2 | **Full mixed per-stage objectives** | `stage1 = flow` + `stage2 = wgan` must actually run — Stage 1 is good as flow, Stage 2 is what is being iterated on. The train loop becomes one trainer object per stage, each owning its optimizers and update cadence. | | 3 | **Migration shim for configs *and* checkpoints** | Nothing on `/ceph` goes dead. v0.2 `config.toml` files and v0.2 `model_config` dicts are translated on load. | | 4 | **Nested objective sub-tables** | `[stage1_model.wgan]`, `[stage2_model.ddpm]` rather than flat `wgan_n_critic` keys — self-documenting about which keys the active generator ignores, and validation can warn on a populated sub-table that is never read. | -| 5 | **Particle type is adversarial: straight-through Gumbel into the critic** | The critic sees a relaxed one-hot alongside energy/direction, so the joint (species, kinematics) distribution is learned rather than factorized. Makes the collapse hypothesis directly testable instead of assumed. | +| 5 | **Particle type is adversarial: straight-through Gumbel into the critic** | The critic sees a relaxed one-hot alongside energy/direction, so the joint (species, kinematics) distribution is learned rather than factorized. Makes the collapse hypothesis directly testable instead of assumed. Accepts a **biased** gradient through the type path — assumed negligible, with a validation obligation in §11.4. | | 6 | **No charge conservation in v0.3.0** | Explicitly "not yet worked out" in the meeting. No `[stage2_model.conservation]` block at all. Energy conservation stays exact and implicit in the stick-breaking encoding. | | 7 | **Explicit stage-prefixed CLI flags** | `--stage2-hidden-dim` etc., no generic `--set path=value`. Discoverable via `--help` and tab-completable; the cost is a flag list kept in sync with `DEFAULT_CONFIG` by hand. | | 8 | **AR history is a config axis, markov default** | Markov (previous token + remaining budget + slot index) is the baseline that makes the meeting's §6 "is attention useful" question answerable by ablation rather than by comparing differently-shaped models. Both sit behind one `history_encoder(prefix) -> vector` interface. | @@ -907,16 +907,50 @@ All in `giant/config.py`: the implementation flow (§12 step 8), measured on real hardware using the example configs — not guessed from the existing calibration constants. -### 11.4 Still open +### 11.4 Accepted with a validation obligation: differentiability -- **Differentiability sanity check with Jan.** The meeting's §5 notes that the - original argument for the continuous type target rested on avoiding a - non-differentiable categorical *sampling* step — but per-token training loss is - differentiable either way under teacher forcing, and full shower-rollout backprop - is already structurally non-differentiable once secondaries spawn branches. The - note flags this as "worth confirming explicitly with Jan rather than assuming - it". Decision 5 rests on this reasoning, so it is worth raising before step 4 - rather than after. +**Position taken: differentiability through the categorical type path is broken, +and that is accepted.** The expected contribution of the broken path to the total +gradient is small enough to ignore. **This is an assumption, not a result — it has +to be demonstrated later.** + +Recording it precisely, since "broken" covers three distinct things: + +| where | status | +|-------|--------| +| Per-token training loss under teacher forcing | **Fine.** Softmax cross-entropy needs no sampling; the loss is differentiable in the logits. | +| ST-Gumbel into the critic (`generator = "wgan"`, §2.1) | **Biased, not absent.** The forward pass is a hard one-hot; the backward pass pretends it was the soft sample. Gradient flows, but it is not the gradient of what was actually computed. | +| Full shower-rollout backprop | **Structurally broken regardless.** Already non-differentiable once secondaries spawn branches, independent of the type representation — so the categorical switch costs nothing that was not already lost. | + +The claim being accepted is about the middle row: the straight-through estimator's +bias, propagated into the shared trunk, is expected to be negligible against the +gradient from the continuous paths (stick-breaking energy, direction, and — when +stage 1 is active — the 9D primary target). The third row is the reason this is +tolerable at all: end-to-end differentiability was never available. + +**Validation obligation.** Do not treat this as settled until one of the following +has actually been run. Cheapest first: + +1. **Gradient-magnitude accounting.** Instrument a training run to log the norm of + the trunk gradient contributed through the type slice against the norm from the + continuous slices. "Negligible" should mean a stable, small ratio — not merely + small at initialization. This is the direct measurement of the claim and costs + almost nothing to add. +2. **Detached-type ablation.** Train with the type path detached from the shared + trunk entirely (type head still learns; no type gradient reaches the trunk) + against the ST-Gumbel default. Comparable species marginals and kinematics mean + the coupling was weak, which is the same conclusion by a different route. +3. **Estimator swap**, only if 1–2 are inconclusive: compare ST-Gumbel against an + unbiased-but-high-variance estimator (e.g. REINFORCE with a baseline) on a short + run. Agreement in the learned marginals means the bias did not matter. + +Option 1 should be added when the AR trunk lands (step 5), so the evidence accrues +during the architecture comparison rather than needing a dedicated run afterwards. + +**Why it still matters that this is written down:** decision 5 (adversarial type +via ST-Gumbel) rests on this assumption. If the ratio in test 1 turns out not to be +small, the fallback is not a redesign — it is `particle_type.target = "physical"` +or a non-adversarial CE head, both of which already exist as config options. --- @@ -935,7 +969,9 @@ All in `giant/config.py`: action item 1, and it is testable against the one-shot baseline before any AR work. 5. **`Stage2Autoregressive`** with `history = "markov"`, `teacher_forcing = - "always"`. The meeting's step 3. + "always"`. The meeting's step 3. Add the gradient-magnitude instrumentation of + §11.4 test 1 here, so the differentiability assumption accrues evidence during + the architecture comparison instead of needing its own run later. 6. **`sample.py` / `rollout.py`** — AR generation and class -> PDG decode, so an AR model can actually be rolled out and put through `giant analyze`. Includes the L1-distance diagnostic of §11.3. -- 2.39.5 From eb6dd274062f27da2af97d00c539970b3e691127 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 10:15:20 +0200 Subject: [PATCH 04/30] v0.3.0 step 1: new nested config schema, v0.2 migration shim MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the single global train.mode + [model] block with the four top-level blocks docs/v0.3.0-design.md specifies ([conditioning], [stage1_model], [stage2_model], [train]), so Stage 1 and Stage 2 can run independent generative objectives and Stage 2 can train standalone. - migrate_config translates old config.toml/checkpoint dicts on load, so nothing on /ceph goes dead; loudly rejects non-zero expert_hidden_dim/expert_n_blocks, which v0.3.0 no longer supports. - merge_cli_overrides/save_config generalize from one hardcoded nesting level (model.router) to arbitrary recursive depth. - default_out_dir_name candidates move to dotted paths against the new schema, with per-stage router/generator discriminators. - validate_config adds cross-block checks the per-block schema can't express (particle_type.target=embedding needs a matching conditioning mode, tie_to_stage1 needs an active stage 1, etc). - resolve_expert_dims is deleted (experts always inherit the stage's hidden_dim/n_res_blocks now) — pipeline.py/cli.py callers are left dangling on purpose, to be updated in the network.py/train.py steps that follow. Co-Authored-By: Claude Sonnet 5 --- giant/config.py | 878 ++++++++++++++++++++++++++++++++----------- tests/test_config.py | 797 ++++++++++++++++++++++++++++----------- 2 files changed, 1236 insertions(+), 439 deletions(-) diff --git a/giant/config.py b/giant/config.py index e4945e5..77814af 100644 --- a/giant/config.py +++ b/giant/config.py @@ -1,3 +1,4 @@ +import copy import hashlib import random import subprocess @@ -12,133 +13,287 @@ import torch class Conditioning(str, Enum): - """`model.conditioning` choices — shared by `giant.cli` and `scripts.dwarf`'s - Typer commands so the two CLIs can't silently drift apart on the option's - valid values (see DEFAULT_CONFIG["model"]["conditioning"] for what each - value means).""" + """`conditioning.particle.type` / `conditioning.material.type` choices — + shared by `giant.cli` and `scripts.dwarf`'s Typer commands so the two + CLIs can't silently drift apart on the option's valid values (see + DEFAULT_CONFIG["conditioning"] for what each value means).""" physical = "physical" embedding = "embedding" + onehot = "onehot" + + +# Tags a config dict (config.toml, or a checkpoint's model_config) as the new +# v0.3 nested format. Absence of `[meta].config_version == CONFIG_VERSION` is +# read as "this is a v0.2 dict" by migrate_config below. +CONFIG_VERSION = 3 DEFAULT_CONFIG: dict = { - "train": { - "mode": "flow", - "epochs": 100, - "batch_size": 4096, - "lr": 3e-4, - "weight_decay": 0.01, # AdamW default — exposed so it can be tuned - "ema_decay": 0.9999, # EMA of model weights for sampling; 0 disables - # per-epoch val loss (not the marginal/KL validate_every pass) is - # capped to this many batches; 0 = full val set every epoch - "max_val_batches": 200, - "val_fraction": 0.1, - "num_workers": 4, - "seed": 0, - "validate_every": 10, - "validate_steps": 10, - "warmup_epochs": 5, - "lambda_nsec": 0.1, - "lambda_s2": 1.0, - # WGAN-GP-only knobs (mode == "wgan"; ignored by flow/ddpm). n_critic: - # critic updates per generator update. gp_weight: gradient-penalty - # coefficient (Gulrajani et al. 2017). critic_lr: 0.0 means "use - # `lr`" — not None, since save_config's TOML writer has no null - # literal to round-trip. - "n_critic": 5, - "gp_weight": 10.0, - "critic_lr": 0.0, - # Weights & Biases per-epoch metric logging (opt-in; see giant.train). - # "" for wandb_run_name means "use the checkpoint out_dir name" — not - # None, since save_config's TOML writer has no null literal to - # round-trip. - "wandb": False, - "wandb_project": "giant", - "wandb_run_name": "", - # Batch-granularity metrics (loss/grad_norm/lr) are logged every N - # optimizer steps, not every batch — a single epoch can be tens of - # thousands of steps (see steps_per_epoch above), and logging every - # one of them would flood the run with points the UI has to downsample - # anyway. Per-epoch metrics (the metrics.csv row) always log in full. - "wandb_log_every": 50, + "conditioning": { + # Width of the fused conditioning vector produced by the encoder's + # fusion MLP, consumed by every downstream trunk. + "out_dim": 128, + # false: stage 1 and stage 2 each construct their own ConditionEncoder + # with identical config but independent weights. true: one instance, + # shared by reference (halves the conditioning parameter count, + # forces a common representation). + "share_stages": False, + "particle": { + # "physical": a small MLP over log(mass)/charge, computable for + # any PDG code — generalizes beyond the training menu. + # "embedding": a learned nn.Embedding over a dense training-vocab + # index — memorizes the training menu; the generalization- + # comparison baseline, and the only mode compatible with + # stage2_model.particle_type.target = "embedding". + # "onehot": a fixed, unlearned vector — top (emb_dim - 1) PDG + # codes by training-set count, plus one "other" bin. NOT a + # reparameterization of "embedding": the vocabulary cap is the + # real difference. + "type": "physical", + # Width of this axis's vector. Under "onehot" this also sets the + # class count. + "emb_dim": 16, + # Depth of the sub-MLP under "physical". Ignored under + # "embedding"/"onehot". + "n_layers": 1, + }, + "material": { + "type": "physical", + "emb_dim": 16, + "n_layers": 1, + }, }, - "model": { + "stage1_model": { + # false skips building/training stage 1 entirely. The resulting + # checkpoint holds only stage 2 and cannot be rolled out. + "active": True, + # "flow": conditional flow matching (~10 ODE steps at inference). + # "ddpm": cosine-schedule diffusion baseline. + # "wgan": WGAN-GP, single forward pass at inference. + "generator": "flow", + # Trunk width — also the width of every expert under a routed trunk. "hidden_dim": 256, - "n_blocks": 6, - "emb_dim": 16, - "dropout": 0.1, - # WGAN generator noise-vector width (mode == "wgan" only). - "noise_dim": 64, - # "physical" conditions on material/particle physical properties via - # a small MLP (giant.model.network.ConditionEncoder); "embedding" - # keeps the original learned pdg/material embedding tables — kept - # available as the generalization-comparison baseline. Checkpoints - # from before this option existed have no "conditioning" key and - # load as "embedding" (see giant.model.network.build_models). - "conditioning": "physical", + # Number of ResBlocks in the trunk, and in every expert under a + # routed trunk. + "n_res_blocks": 6, + "dropout": 0.0, + # Weight of this stage's loss in the total when both stages are + # active and non-adversarial. A WGAN stage's adversarial loss drives + # its own optimizer, so `lambda` scales only its non-adversarial + # auxiliary terms. + "lambda": 1.0, + "flow": { + # Width of the SinusoidalEmbedding for the flow time variable. + "time_dim": 64, + }, + "ddpm": { + "time_dim": 64, + "n_steps": 1000, + }, + "wgan": { + "noise_dim": 64, + "n_critic": 5, + "gp_weight": 10.0, + # 0.0 means "inherit train.lr" — not None, since the TOML writer + # has no null literal to round-trip. + "critic_lr": 0.0, + # 0 means "inherit stage1_model.hidden_dim/n_res_blocks" — same + # round-trip-friendly sentinel as critic_lr above. + "critic_hidden_dim": 0, + "critic_n_res_blocks": 0, + }, "router": { "enabled": False, "type": "energy", # selects the Router impl from ROUTER_REGISTRY "n_experts": 4, - # 0 means "inherit model.hidden_dim/n_blocks" (see - # resolve_expert_dims below) — not a fixed 128/3, which silently - # ignored --hidden-dim/--n-blocks whenever routing was enabled. - # TOML has no null literal to round-trip (same pattern as - # critic_lr/wandb_run_name above), hence 0 rather than None. - "expert_hidden_dim": 0, - "expert_n_blocks": 0, "temperature": 0.5, # energy/pdg-router kwarg "learn_centers": True, # energy/pdg-router kwarg # energy-router kwargs: mutually exclusive optional learnable - # gate-sharpness modes (see giant.model.network.EnergyRouter). - # learn_width generalizes the shared `temperature` to one - # learnable width per expert; learn_temperature instead makes - # the single shared `temperature` itself learnable. Both are - # bounded to [width_min_ratio, width_max_ratio] * temperature - # (sigmoid-parameterized, warm-started to reproduce `temperature` - # exactly at init) so gate sharpness can't run away to a - # collapse-inducing extreme during training. + # gate-sharpness modes. learn_width generalizes the shared + # `temperature` to one learnable width per expert; + # learn_temperature instead makes the single shared `temperature` + # itself learnable. Both are bounded to [width_min_ratio, + # width_max_ratio] * temperature so gate sharpness can't run away + # to a collapse-inducing extreme during training. "learn_width": False, "learn_temperature": False, "width_min_ratio": 0.1, "width_max_ratio": 10.0, - "lambda_balance": 0.0, # optional load-balance aux loss weight - # optional entropy-regularization aux loss weight (generic - # Router.entropy_loss, penalizes uniform/collapsed gating) — a - # secondary guard against all experts' widths/temperature - # co-inflating together, which lambda_balance alone can't see - # since per-expert usage shares stay even throughout that - # failure mode. Off by default; bounding above is the primary - # defense. See giant.model.network.Router.entropy_loss. + # Importance-CV^2 load-balancing aux loss weight (Shazeer et al. + # 2017). + "lambda_balance": 0.0, + # Entropy-regularization weight penalizing uniform/collapsed + # gating — a secondary guard against all experts' widths + # co-inflating together, which lambda_balance alone can't see. "lambda_entropy": 0.0, - # Opt-in straight-through Gumbel-softmax train-time combine weights - # (see giant.model.network.Router.combine_weights): the training - # forward pass samples a hard one-hot combination — matching - # eval-time top-1 dispatch exactly — while the backward pass still - # flows a smooth gradient to every expert. Targets the train/eval - # mismatch identified as a likely contributor to experts - # overlapping instead of partitioning (see CLAUDE.md roadmap). - # gumbel_tau_start/_end are annealed linearly over training - # (giant.train._gumbel_tau); off by default, no effect unless - # gumbel = true. + # Opt-in straight-through Gumbel-softmax train-time combine + # weights: the training forward pass samples a hard one-hot + # combination (matching eval-time top-1 dispatch exactly) while + # the backward pass still flows smooth gradient to every expert. "gumbel": False, "gumbel_tau_start": 1.0, "gumbel_tau_end": 0.1, "emb_dim": 8, # process/pdg-router kwarg: own pdg(/mat) embedding width "hidden_dim": 64, # process-router kwarg: its classifier's hidden width "lambda_proc": 0.0, # process-router kwarg: supervised process-CE weight - # (0.0 still trains a working router — the gate gets gradient - # through the downstream flow loss like EnergyRouter's centers — - # but only lambda_proc > 0 grounds it in the true `process` label) # type = "composed" routes on multiple axes at once (e.g. energy x # pdg), each with its own expert count/hyperparameters. Axes are - # NOT in these defaults (there's no meaningful default axis list) - # — set them as flat axis{i}_{field} keys instead of "n_experts", - # e.g. axis0_type = "energy", axis0_n_experts = 4, axis1_type = - # "pdg", axis1_n_experts = 3, axis1_emb_dim = 8. See - # giant.model.network._parse_composed_axes / `--router-axis`. + # NOT in these defaults — set them as flat axis{i}_{field} keys + # instead of "n_experts", e.g. axis0_type = "energy", + # axis0_n_experts = 4, axis1_type = "pdg", axis1_n_experts = 3, + # axis1_emb_dim = 8. See giant.model.network._parse_composed_axes. }, }, + "stage2_model": { + # false trains stage 1 alone. giant rollout must then refuse the + # checkpoint; giant predict still works. + "active": True, + # "one_shot": predict all k_max slots simultaneously with padded + # slots masked from the loss (v0.2 behaviour). + # "autoregressive": emit one secondary at a time in descending-energy + # order. + "decoder": "autoregressive", + # As stage1_model.generator, but under "autoregressive" this is the + # objective for each token. + "generator": "wgan", + "hidden_dim": 256, + "n_res_blocks": 6, + "dropout": 0.0, + "lambda": 1.0, + # Maximum secondary slots. Under "one_shot" this is the fixed output + # width; under "autoregressive" it is a safety cap on the generation + # loop. + "k_max": 15, + # Width of the projected stage-1 outcome fed into stage 2's + # conditioning. + "context_dim": 64, + # "truth": the ground-truth stage-1 target vector, detached — stage- + # level teacher forcing (v0.2 behaviour). "sampled": stage 1's own + # sampled output, closing the train/inference gap at the cost of a + # sampling pass per batch and a moving target early in training. + "stage1_context": "truth", + "n_sec": { + # "head": a classifier over {0..k_max} on the condition encoding + # alone (no diffusion noise), callable independently at + # inference. + # "stop_token": an EOS-style implicit stop — accepted by the + # schema but not implemented in v0.3.0 (see validate_config). + # "truth": take n_sec from ground truth — standalone stage-2 + # evaluation only, never for rollout. + "mode": "head", + "lambda": 0.1, # cross-entropy weight for the head + }, + "particle_type": { + # The three targets mirror the three conditioning.particle + # modes. "onehot": class logits over conditioning.particle.emb_dim + # classes. "physical": regressed (log mass, charge). "embedding": + # regressed against conditioning's own particle embedding table + # (requires conditioning.particle.type = "embedding"). + "target": "onehot", + "lambda": 1.0, + # How a predicted "other" class becomes a concrete PDG code at + # rollout. "sample": draw from the empirical within-bucket + # distribution recorded at map-build time. "modal": always the + # most common member. "drop": discard the secondary. Read only + # under target = "onehot". + "other_policy": "sample", + }, + "autoregressive": { + # Canonical generation order. Single-valued for now; the key + # exists so an alternative ordering is not a config break. + "order": "energy_desc", + # How token i+1 sees tokens <= i. "markov": previous token plus + # running scalars (remaining energy budget, slot index) — a + # fixed-width summary. "attention": causal self-attention over + # all emitted tokens. + "history": "markov", + # "always": condition on the ground-truth previous secondary + # throughout training. "scheduled": scheduled sampling — + # interpolate toward the model's own prediction. "never": + # free-running from the start. + "teacher_forcing": "always", + "tf_p_start": 1.0, + "tf_p_end": 1.0, + "attn_n_heads": 4, + "attn_n_layers": 2, + }, + "flow": { + "time_dim": 64, + }, + "ddpm": { + "time_dim": 64, + "n_steps": 1000, + }, + "wgan": { + "noise_dim": 64, + "n_critic": 5, + "gp_weight": 10.0, + "critic_lr": 0.0, + "critic_hidden_dim": 0, + "critic_n_res_blocks": 0, + # Straight-through Gumbel temperature for the particle-type + # one-hot (distinct from router.gumbel_tau_start/_end, which + # anneal expert-combination weights). Read only under + # particle_type.target = "onehot". + "gumbel_tau_start": 1.0, + "gumbel_tau_end": 0.1, + }, + "router": { + # true: stage 2 shares stage 1's Router module instance, so + # expert i in stage 1 and expert i in stage 2 gate on identical + # conditions by construction — every other key in this block is + # then ignored. false: an independent router. + "tie_to_stage1": False, + "enabled": False, + "type": "energy", + "n_experts": 4, + "temperature": 0.5, + "learn_centers": True, + "learn_width": False, + "learn_temperature": False, + "width_min_ratio": 0.1, + "width_max_ratio": 10.0, + "lambda_balance": 0.0, + "lambda_entropy": 0.0, + "lambda_proc": 0.0, + "gumbel": False, + "gumbel_tau_start": 1.0, + "gumbel_tau_end": 0.1, + "emb_dim": 8, + "hidden_dim": 64, + }, + }, + "train": { + "epochs": 100, + "batch_size": 4096, + "lr": 3e-4, + "weight_decay": 0.01, # AdamW default — exposed so it can be tuned + "ema_decay": 0.9999, # EMA of model weights for sampling; 0 disables + "warmup_epochs": 5, + "val_fraction": 0.1, + # per-epoch val loss (not the marginal/KL validate_every pass) is + # capped to this many batches; 0 = full val set every epoch + "max_val_batches": 200, + "num_workers": 4, + "seed": 0, + "validate_every": 10, + "validate_steps": 10, + # Weights & Biases per-epoch metric logging. Default true in v0.3.0 + # (was opt-in false): the v0.3.0 work is a sequence of architecture + # comparisons, and a run that wasn't logged isn't comparable. Set + # false for throwaway/debug runs. + "wandb": True, + "wandb_project": "giant", + # "" means "use the checkpoint out_dir name" — not None, since the + # TOML writer has no null literal to round-trip. + "wandb_run_name": "", + # Batch-granularity metrics (loss/grad_norm/lr) are logged every N + # optimizer steps, not every batch — a single epoch can be tens of + # thousands of steps. Per-epoch metrics (the metrics.csv row) always + # log in full. + "wandb_log_every": 50, + }, } @@ -169,6 +324,9 @@ def auto_device() -> torch.device: # Activation memory is assumed to scale linearly with # batch_size * hidden_dim * n_blocks (the ResBlock stack dominates), so this # is a rough estimate rather than a guaranteed bound. +# NOTE: not yet recalibrated for the v0.3.0 autoregressive stage-2 trunk — +# see docs/v0.3.0-design.md §9/§11.3, deliberately last in the implementation +# order. _REF_BYTES = 7683 * 1024**2 _REF_BATCH_SIZE = 29696 _REF_HIDDEN_DIM = 1024 @@ -254,13 +412,15 @@ def warn_if_git_hash_mismatch(file_cfg: dict, config_path: Path) -> None: def load_checkpoint_config(ckpt_path: str | Path) -> dict: - """Load the full ``[train]``/``[model]``/``[meta]`` config.toml written - alongside a checkpoint by ``save_config``. + """Load the full config.toml written alongside a checkpoint by + ``save_config``. Returns ``{}`` if no config.toml sits next to the checkpoint (older runs, or a checkpoint moved without its sidecar) — this is best-effort provenance for threading into a rollout's YAML sidecar, not a hard - requirement for using the checkpoint itself. + requirement for using the checkpoint itself. Returned as-loaded (v0.2 or + v0.3 shape); callers that need the v0.3 shape should run it through + `migrate_config` themselves. """ config_path = Path(ckpt_path).parent / "config.toml" if not config_path.exists(): @@ -283,130 +443,413 @@ def warn_if_checkpoint_config_mismatch(ckpt_path: str | Path) -> None: warn_if_git_hash_mismatch(load_toml(config_path), config_path) +def _get_path(d: dict, dotted: str): + """Read a dotted path (e.g. "stage1_model.router.enabled") out of a + nested dict. Returns None if any component along the path is missing.""" + cur = d + for part in dotted.split("."): + if not isinstance(cur, dict) or part not in cur: + return None + cur = cur[part] + return cur + + +def _set_path(d: dict, dotted: str, value) -> None: + """Write a dotted path into a nested dict, creating intermediate dicts as + needed.""" + parts = dotted.split(".") + cur = d + for part in parts[:-1]: + cur = cur.setdefault(part, {}) + cur[parts[-1]] = value + + +def _deep_merge(base: dict, override: dict) -> dict: + """Recursively merge `override` onto a copy of `base`. + + Dict-valued keys recurse instead of being replaced wholesale, so + overriding one leaf (e.g. stage1_model.router.enabled) never drops + untouched siblings — the rest of stage1_model.router, or of + stage1_model — the same property v0.2's router-only bespoke merge had, + generalized here to arbitrary depth. + """ + result = dict(base) + for k, v in override.items(): + if isinstance(v, dict) and isinstance(result.get(k), dict): + result[k] = _deep_merge(result[k], v) + else: + result[k] = v + return result + + +# v0.2 [train] keys that pass through to v0.3 [train] unchanged (same name, +# same meaning) when present in the loaded file — everything model-shaped +# moved to the stage/conditioning blocks instead (see the rest of +# migrate_config below). +_V02_TRAIN_PASSTHROUGH = ( + "epochs", + "batch_size", + "lr", + "weight_decay", + "ema_decay", + "max_val_batches", + "val_fraction", + "num_workers", + "seed", + "validate_every", + "validate_steps", + "warmup_epochs", + "wandb", + "wandb_project", + "wandb_run_name", + "wandb_log_every", +) + +# v0.2 model.hidden_dim/n_blocks/dropout applied identically to both stages +# (there was only ever one trunk shape) -> copied to both stage{1,2}_model. +_V02_MODEL_TO_BOTH_STAGES = ( + ("hidden_dim", "hidden_dim"), + ("n_blocks", "n_res_blocks"), + ("dropout", "dropout"), +) + +# v0.2 train.{n_critic,gp_weight,critic_lr} applied identically to both +# stages' wgan sub-table (there was only ever one wgan objective, shared). +_V02_TRAIN_TO_BOTH_STAGES_WGAN = ( + ("n_critic", "n_critic"), + ("gp_weight", "gp_weight"), + ("critic_lr", "critic_lr"), +) + + +def migrate_config(cfg: dict) -> dict: + """Translate a v0.2 config dict (single [train] + [model]) into the v0.3 + nested format ([conditioning]/[stage1_model]/[stage2_model]/[train]). + + Called on every config.toml load (see merge_cli_overrides) so old + training configs on disk keep working under new code without hand- + editing (decision 3, docs/v0.3.0-design.md §4). `[meta].config_version == + CONFIG_VERSION` marks a dict as already-v0.3; its absence is read as + "this is v0.2" (the design doc's stated rule), so an already-migrated + dict is returned unchanged (deep-copied). + + Only keys actually present in `cfg` are translated — `cfg` may be a + partial file (e.g. `[train]\\nepochs = 5\\n` with no [model] section at + all, relying on v0.2 defaults for everything else). Separately, a fixed + set of v0.2 architectural facts that were never exposed as config keys at + all (e.g. the conditioning MLP was always 2 layers deep, not the v0.3 + default of 1) are injected unconditionally whenever this function decides + it is migrating a v0.2 dict, regardless of which keys the file happened + to set. + + Operates on the config.toml shape. A checkpoint's `model_config` dict + (which additionally carries n_sec_head ownership, §4.1, and needs + `network.build_models`'s cooperation) is a separate migration surface, + deferred to the network.py refactor. + """ + if _get_path(cfg, "meta.config_version") == CONFIG_VERSION: + return copy.deepcopy(cfg) + + cfg = copy.deepcopy(cfg) + old_train = cfg.pop("train", {}) + old_model = cfg.pop("model", {}) + old_router = dict(old_model.pop("router", {})) + + new: dict = {} + + for key in _V02_TRAIN_PASSTHROUGH: + if key in old_train: + _set_path(new, f"train.{key}", old_train[key]) + + if "mode" in old_train: + _set_path(new, "stage1_model.generator", old_train["mode"]) + _set_path(new, "stage2_model.generator", old_train["mode"]) + if "lambda_nsec" in old_train: + _set_path(new, "stage2_model.n_sec.lambda", old_train["lambda_nsec"]) + if "lambda_s2" in old_train: + _set_path(new, "stage2_model.lambda", old_train["lambda_s2"]) + for old_key, new_key in _V02_TRAIN_TO_BOTH_STAGES_WGAN: + if old_key in old_train: + _set_path(new, f"stage1_model.wgan.{new_key}", old_train[old_key]) + _set_path(new, f"stage2_model.wgan.{new_key}", old_train[old_key]) + + for old_key, new_key in _V02_MODEL_TO_BOTH_STAGES: + if old_key in old_model: + _set_path(new, f"stage1_model.{new_key}", old_model[old_key]) + _set_path(new, f"stage2_model.{new_key}", old_model[old_key]) + if "emb_dim" in old_model: + _set_path(new, "conditioning.particle.emb_dim", old_model["emb_dim"]) + _set_path(new, "conditioning.material.emb_dim", old_model["emb_dim"]) + if "conditioning" in old_model: + _set_path(new, "conditioning.particle.type", old_model["conditioning"]) + _set_path(new, "conditioning.material.type", old_model["conditioning"]) + if "noise_dim" in old_model: + _set_path(new, "stage1_model.wgan.noise_dim", old_model["noise_dim"]) + _set_path(new, "stage2_model.wgan.noise_dim", old_model["noise_dim"]) + if "k_max" in old_model: + _set_path(new, "stage2_model.k_max", old_model["k_max"]) + + if old_router: + expert_hidden_dim = old_router.pop("expert_hidden_dim", 0) + expert_n_blocks = old_router.pop("expert_n_blocks", 0) + if expert_hidden_dim or expert_n_blocks: + raise ValueError( + "v0.2 config sets model.router.expert_hidden_dim/" + f"expert_n_blocks to a non-default value " + f"({expert_hidden_dim!r}, {expert_n_blocks!r}); v0.3.0 removed " + "per-expert sizing (experts always inherit the stage's " + "hidden_dim/n_res_blocks), so this config's routed experts " + "have a different width/depth than the monolith and its " + "checkpoint can only be loaded by v0.2 code." + ) + _set_path(new, "stage1_model.router", dict(old_router)) + stage2_router = dict(old_router) + stage2_router["tie_to_stage1"] = False + _set_path(new, "stage2_model.router", stage2_router) + + # v0.2 architectural facts with no corresponding config key at all — + # always set once we've determined we're migrating a v0.2 dict, + # independent of what the file did/didn't specify. NOTE: n_layers here + # (2) differs from the v0.3 *default* (1) — this is not a typo, see the + # docstring above. + _set_path(new, "conditioning.out_dim", 128) + _set_path(new, "conditioning.particle.n_layers", 2) + _set_path(new, "conditioning.material.n_layers", 2) + _set_path(new, "stage1_model.active", True) + _set_path(new, "stage1_model.flow.time_dim", 64) + _set_path(new, "stage1_model.ddpm.time_dim", 64) + _set_path(new, "stage2_model.active", True) + _set_path(new, "stage2_model.flow.time_dim", 64) + _set_path(new, "stage2_model.ddpm.time_dim", 64) + _set_path(new, "stage2_model.context_dim", 64) + _set_path(new, "stage2_model.decoder", "one_shot") + _set_path(new, "stage2_model.particle_type.target", "physical") + + new_meta = dict(cfg.pop("meta", {})) + new_meta["config_version"] = CONFIG_VERSION + new["meta"] = new_meta + + # Anything else in the original dict (unrecognized top-level sections) + # carries through untouched rather than being silently dropped. + for k, v in cfg.items(): + new.setdefault(k, v) + + return new + + def merge_cli_overrides( defaults: dict, config_path: Path | None, - train_overrides: dict, - model_overrides: dict, + overrides: dict, ) -> dict: - """Resolve config as defaults -> TOML file -> explicit CLI flags. + """Resolve config as defaults -> TOML file -> explicit overrides. - `model.router` is deep-merged one level (rather than replaced wholesale) - at each stage, so a TOML file or CLI flag only overriding e.g. - `router.enabled` doesn't drop the rest of the router defaults. + `overrides` is keyed by top-level section name (e.g. "stage1_model", + "train"), each value an arbitrarily nested dict of overrides to + deep-merge (see `_deep_merge`) — the shape stage-prefixed CLI flags + naturally produce. A v0.2-shaped TOML file is transparently migrated + (`migrate_config`) before merging, so old configs on disk keep working + under the new schema. """ - cfg = {"train": dict(defaults["train"]), "model": dict(defaults["model"])} - cfg["model"]["router"] = dict(defaults["model"]["router"]) + cfg = copy.deepcopy(defaults) if config_path is not None: - file_cfg = load_toml(config_path) - cfg["train"].update(file_cfg.get("train", {})) - file_model = dict(file_cfg.get("model", {})) - file_router = file_model.pop("router", None) - cfg["model"].update(file_model) - if file_router: - cfg["model"]["router"].update(file_router) + file_cfg = migrate_config(load_toml(config_path)) + for section, values in file_cfg.items(): + if section == "meta": + continue + if isinstance(values, dict): + cfg[section] = _deep_merge(cfg.get(section, {}), values) + else: + cfg[section] = values warn_if_git_hash_mismatch(file_cfg, config_path) - model_overrides = dict(model_overrides) - router_overrides = model_overrides.pop("router", None) - cfg["train"].update(train_overrides) - cfg["model"].update(model_overrides) - if router_overrides: - cfg["model"]["router"].update(router_overrides) + for section, values in overrides.items(): + if isinstance(values, dict): + cfg[section] = _deep_merge(cfg.get(section, {}), values) + else: + cfg[section] = values return cfg -def resolve_expert_dims( - router_cfg: dict, hidden_dim: int, n_blocks: int -) -> tuple[int, int]: - """Resolve a router's expert hidden_dim/n_blocks, inheriting from the - monolith's when left at the 0 ("unset") sentinel. +def validate_config(cfg: dict) -> None: + """Cross-block validation the per-block schema can't express on its own. - Used by both `giant.pipeline` (to build the checkpoint's `model_config`) - and `giant.cli`'s batch-size auto-estimate, so `--hidden-dim`/`--n-blocks` - size the experts the same way in both places unless - `router.expert_hidden_dim`/`expert_n_blocks` are explicitly overridden. + Raises ValueError with a clear message on the first violation found. Call + after `merge_cli_overrides` has produced a fully-merged v0.3 config — + these checks need to see across blocks, so they don't belong in + `migrate_config` (which only ever sees one dict's own keys) or in any + single block's defaults. """ - expert_hidden_dim = router_cfg.get("expert_hidden_dim") or hidden_dim - expert_n_blocks = router_cfg.get("expert_n_blocks") or n_blocks - return expert_hidden_dim, expert_n_blocks + particle_type = _get_path(cfg, "conditioning.particle.type") + + pt_target = _get_path(cfg, "stage2_model.particle_type.target") + if pt_target == "embedding" and particle_type != "embedding": + raise ValueError( + "stage2_model.particle_type.target = 'embedding' requires " + "conditioning.particle.type = 'embedding' (there is no embedding " + "table to regress against under conditioning.particle.type = " + f"{particle_type!r})" + ) + + for stage_name in ("stage1_model", "stage2_model"): + router = _get_path(cfg, f"{stage_name}.router") or {} + if ( + router.get("enabled") + and router.get("type") in ("pdg", "process") + and particle_type == "physical" + ): + raise ValueError( + f"{stage_name}.router.type = {router['type']!r} builds its " + "own training-vocab-scoped embedding, incompatible with " + "conditioning.particle.type = 'physical' (defeats " + "generalization beyond the training menu) — pick a " + "different router type or a different " + "conditioning.particle.type" + ) + + if _get_path(cfg, "stage2_model.router.tie_to_stage1") and not _get_path( + cfg, "stage1_model.active" + ): + raise ValueError( + "stage2_model.router.tie_to_stage1 = true requires " + "stage1_model.active = true (there is no stage-1 router to tie to)" + ) + + if _get_path(cfg, "stage2_model.n_sec.mode") == "stop_token": + raise ValueError( + "stage2_model.n_sec.mode = 'stop_token' is accepted by the schema " + "but not implemented in v0.3.0 — use 'head' (default) or 'truth' " + "(standalone stage-2 evaluation only, never for rollout)" + ) -_CONDITIONING_CODE = {"physical": "phys", "embedding": "emb"} +_CONDITIONING_CODE = {"physical": "phys", "embedding": "emb", "onehot": "oh"} -# Priority-ordered candidate fields for default_out_dir_name: (label, getter, -# formatter). `getter(train, model)` returns None when the field is at its -# default (and so should be omitted); otherwise formatter(value) renders the -# name token. The router is a single unit gated on `router.enabled` rather -# than one candidate per router key, since its type/n_experts are meaningless -# while disabled. -def _mode_candidate(train, model): - return None if train["mode"] == DEFAULT_CONFIG["train"]["mode"] else train["mode"] +def _path_candidate(dotted_path: str, prefix: str, formatter=str): + """Candidate factory: show `prefix + formatter(value)` when the value at + `dotted_path` differs from its DEFAULT_CONFIG value, else omit.""" - -def _router_candidate(train, model): - router = model["router"] - if router["enabled"] == DEFAULT_CONFIG["model"]["router"]["enabled"]: - return None - return f"r-{router['type']}{router['n_experts']}" - - -def _router_flag_candidate(field, token_map): - """Candidate factory for a boolean `model.router` sub-field. - - Gated on `router.enabled` like `_router_candidate` (a disabled router's - sub-fields are meaningless), then omitted unless `field` differs from - its DEFAULT_CONFIG value — same "only show non-default" rule as every - other candidate. `token_map` need only cover the non-default value(s), - since the default value always yields None. - """ - - def _candidate(train, model): - router = model["router"] - default_router = DEFAULT_CONFIG["model"]["router"] - if router["enabled"] == default_router["enabled"]: + def _candidate(cfg): + value = _get_path(cfg, dotted_path) + default = _get_path(DEFAULT_CONFIG, dotted_path) + if value == default: return None - value = router[field] - if value == default_router[field]: - return None - return token_map[value] + return f"{prefix}{formatter(value)}" return _candidate -def _conditioning_candidate(train, model): - if model["conditioning"] == DEFAULT_CONFIG["model"]["conditioning"]: - return None - code = _CONDITIONING_CODE.get(model["conditioning"], model["conditioning"]) - return f"c{code}" - - -def _default_field_candidate(section_key, field, prefix): - def _candidate(train, model): - section = train if section_key == "train" else model - value = section[field] - if value == DEFAULT_CONFIG[section_key][field]: +def _conditioning_candidate(axis: str, short: str): + def _candidate(cfg): + value = _get_path(cfg, f"conditioning.{axis}.type") + default = _get_path(DEFAULT_CONFIG, f"conditioning.{axis}.type") + if value == default: return None - return f"{prefix}{value}" + code = _CONDITIONING_CODE.get(value, value) + return f"{short}{code}" return _candidate +def _router_candidate(stage_key: str, short: str): + """Candidate for a stage's router as a single unit, gated on + `router.enabled` (a disabled router's type/n_experts are meaningless).""" + + def _candidate(cfg): + router = _get_path(cfg, f"{stage_key}.router") or {} + default_router = _get_path(DEFAULT_CONFIG, f"{stage_key}.router") or {} + if router.get("enabled") == default_router.get("enabled"): + return None + return f"{short}r-{router['type']}{router['n_experts']}" + + return _candidate + + +def _router_flag_candidate(stage_key: str, short: str, field: str, token_map: dict): + """Candidate factory for a boolean field inside a stage's router block. + + Gated on `router.enabled` like `_router_candidate`, then omitted unless + `field` differs from its DEFAULT_CONFIG value. `token_map` need only + cover the non-default value(s), since the default value always yields + None. + """ + + def _candidate(cfg): + router = _get_path(cfg, f"{stage_key}.router") or {} + default_router = _get_path(DEFAULT_CONFIG, f"{stage_key}.router") or {} + if router.get("enabled") == default_router.get("enabled"): + return None + value = router.get(field) + if value == default_router.get(field): + return None + return f"{short}{token_map[value]}" + + return _candidate + + +# Priority-ordered candidate fields for default_out_dir_name: (label, +# candidate(cfg) -> str | None). Beyond _OUT_DIR_NAME_MAX_FIELDS non-default +# fields, the remainder collapse into a hash suffix (see +# default_out_dir_name). Candidates read the whole nested cfg via dotted +# paths — there is no single "model" dict anymore now that architecture is +# split across conditioning/stage1_model/stage2_model. _OUT_DIR_NAME_CANDIDATES = [ - ("mode", _mode_candidate), - ("router", _router_candidate), - ("gumbel", _router_flag_candidate("gumbel", {True: "gum"})), - ("learn_centers", _router_flag_candidate("learn_centers", {False: "nolc"})), - ("learn_width", _router_flag_candidate("learn_width", {True: "lw"})), - ("learn_temperature", _router_flag_candidate("learn_temperature", {True: "lt"})), - ("conditioning", _conditioning_candidate), - ("hidden_dim", _default_field_candidate("model", "hidden_dim", "h")), - ("n_blocks", _default_field_candidate("model", "n_blocks", "b")), - ("emb_dim", _default_field_candidate("model", "emb_dim", "e")), - ("lr", _default_field_candidate("train", "lr", "lr")), - ("batch_size", _default_field_candidate("train", "batch_size", "bs")), - ("seed", _default_field_candidate("train", "seed", "seed")), - ("epochs", _default_field_candidate("train", "epochs", "ep")), + ("stage1_generator", _path_candidate("stage1_model.generator", "")), + ("stage2_generator", _path_candidate("stage2_model.generator", "s2-")), + ("stage2_decoder", _path_candidate("stage2_model.decoder", "dec-")), + ( + "stage2_history", + _path_candidate("stage2_model.autoregressive.history", "hist-"), + ), + ( + "particle_type_target", + _path_candidate("stage2_model.particle_type.target", "pt-"), + ), + ("stage1_router", _router_candidate("stage1_model", "s1")), + ("stage2_router", _router_candidate("stage2_model", "s2")), + ( + "stage1_gumbel", + _router_flag_candidate("stage1_model", "s1", "gumbel", {True: "gum"}), + ), + ( + "stage2_gumbel", + _router_flag_candidate("stage2_model", "s2", "gumbel", {True: "gum"}), + ), + ( + "stage1_learn_centers", + _router_flag_candidate("stage1_model", "s1", "learn_centers", {False: "nolc"}), + ), + ( + "stage2_learn_centers", + _router_flag_candidate("stage2_model", "s2", "learn_centers", {False: "nolc"}), + ), + ( + "stage1_learn_width", + _router_flag_candidate("stage1_model", "s1", "learn_width", {True: "lw"}), + ), + ( + "stage2_learn_width", + _router_flag_candidate("stage2_model", "s2", "learn_width", {True: "lw"}), + ), + ( + "stage1_learn_temperature", + _router_flag_candidate("stage1_model", "s1", "learn_temperature", {True: "lt"}), + ), + ( + "stage2_learn_temperature", + _router_flag_candidate("stage2_model", "s2", "learn_temperature", {True: "lt"}), + ), + ("particle_conditioning", _conditioning_candidate("particle", "c")), + ("material_conditioning", _conditioning_candidate("material", "m")), + ("stage1_hidden_dim", _path_candidate("stage1_model.hidden_dim", "h")), + ("stage2_hidden_dim", _path_candidate("stage2_model.hidden_dim", "s2h")), + ("stage1_n_res_blocks", _path_candidate("stage1_model.n_res_blocks", "b")), + ("stage2_n_res_blocks", _path_candidate("stage2_model.n_res_blocks", "s2b")), + ("particle_emb_dim", _path_candidate("conditioning.particle.emb_dim", "e")), + ("lr", _path_candidate("train.lr", "lr")), + ("batch_size", _path_candidate("train.batch_size", "bs")), + ("seed", _path_candidate("train.seed", "seed")), + ("epochs", _path_candidate("train.epochs", "ep")), ] _OUT_DIR_NAME_MAX_FIELDS = 6 @@ -424,11 +867,10 @@ def default_out_dir_name(cfg: dict, now: datetime | None = None) -> str: giant.train), which is the reason a timestamp is always included. """ now = now or datetime.now() - train, model = cfg["train"], cfg["model"] tokens = [] overflow = [] for label, candidate in _OUT_DIR_NAME_CANDIDATES: - token = candidate(train, model) + token = candidate(cfg) if token is None: continue if len(tokens) < _OUT_DIR_NAME_MAX_FIELDS: @@ -476,30 +918,31 @@ def _toml_value(v) -> str: return str(v) -def save_config(cfg: dict, out_dir: Path, meta: dict) -> None: - lines = [] - # One-level-nested dict values (e.g. model.router) are rendered as their - # own [section.subsection] table after the parent section, since TOML - # doesn't accept a bare dict as a `key = value` scalar line. - nested_sections: list[tuple[str, dict]] = [] - for section, values in cfg.items(): - lines.append(f"[{section}]") - for k, v in values.items(): - if isinstance(v, dict): - nested_sections.append((f"{section}.{k}", v)) - continue - lines.append(f"{k:<14} = {_toml_value(v)}") - lines.append("") +def _write_section(lines: list[str], path: str, values: dict) -> None: + """Write one TOML table (`[path]`) and recurse depth-first into any + dict-valued keys as `[path.subkey]` — handles the v0.3 schema's 2-3 level + nesting (e.g. stage1_model.router, stage2_model.n_sec) with no depth + limit, unlike the one-level-only writer this replaces.""" + lines.append(f"[{path}]") + nested: list[tuple[str, dict]] = [] + for k, v in values.items(): + if isinstance(v, dict): + nested.append((f"{path}.{k}", v)) + else: + lines.append(f"{k:<18} = {_toml_value(v)}") + lines.append("") + for sub_path, sub_values in nested: + _write_section(lines, sub_path, sub_values) - for name, values in nested_sections: - lines.append(f"[{name}]") - for k, v in values.items(): - lines.append(f"{k:<14} = {_toml_value(v)}") - lines.append("") + +def save_config(cfg: dict, out_dir: Path, meta: dict) -> None: + lines: list[str] = [] + for section, values in cfg.items(): + _write_section(lines, section, values) lines.append("[meta]") for k, v in meta.items(): - lines.append(f"{k:<14} = {_toml_value(v)}") + lines.append(f"{k:<18} = {_toml_value(v)}") (out_dir / "config.toml").write_text("\n".join(lines)) @@ -514,6 +957,7 @@ def build_run_meta( n_train_steps: int, ) -> dict: return { + "config_version": CONFIG_VERSION, "git_hash": git_hash(), "seed": seed, "timestamp_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"), diff --git a/tests/test_config.py b/tests/test_config.py index 30f6e41..eee0f18 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -1,46 +1,314 @@ from datetime import datetime +from pathlib import Path from giant import config as gconfig +_CONFIGS_DIR = Path(__file__).resolve().parents[1] / "configs" -def _write_config(path, git_hash): - path.write_text( - f""" -[train] -epochs = 5 -[model] -hidden_dim = 64 +def _write_toml(path, git_hash=None, extra=""): + meta = f'\n[meta]\ngit_hash = "{git_hash}"\n' if git_hash is not None else "" + path.write_text(extra + meta) -[meta] -git_hash = "{git_hash}" -""" + +# --------------------------------------------------------------------------- +# Conditioning enum +# --------------------------------------------------------------------------- + + +def test_conditioning_enum_has_onehot(): + assert gconfig.Conditioning.onehot == "onehot" + assert {c.value for c in gconfig.Conditioning} == { + "physical", + "embedding", + "onehot", + } + + +# --------------------------------------------------------------------------- +# _deep_merge +# --------------------------------------------------------------------------- + + +def test_deep_merge_leaf_override_keeps_untouched_siblings(): + base = {"a": 1, "b": {"c": 2, "d": 3}} + result = gconfig._deep_merge(base, {"b": {"c": 99}}) + assert result == {"a": 1, "b": {"c": 99, "d": 3}} + + +def test_deep_merge_recurses_at_multiple_levels(): + base = { + "stage1_model": { + "hidden_dim": 256, + "router": {"enabled": False, "type": "energy", "n_experts": 4}, + } + } + result = gconfig._deep_merge(base, {"stage1_model": {"router": {"enabled": True}}}) + assert result["stage1_model"]["hidden_dim"] == 256 + assert result["stage1_model"]["router"] == { + "enabled": True, + "type": "energy", + "n_experts": 4, + } + + +def test_deep_merge_does_not_mutate_base(): + base = {"a": {"b": 1}} + gconfig._deep_merge(base, {"a": {"b": 2}}) + assert base == {"a": {"b": 1}} + + +def test_deep_merge_non_dict_override_replaces_wholesale(): + base = {"a": {"b": 1}} + result = gconfig._deep_merge(base, {"a": 5}) + assert result == {"a": 5} + + +# --------------------------------------------------------------------------- +# migrate_config +# --------------------------------------------------------------------------- + + +def test_migrate_config_already_v3_returned_unchanged(): + cfg = {"meta": {"config_version": 3}, "stage1_model": {"generator": "flow"}} + result = gconfig.migrate_config(cfg) + assert result == cfg + result["stage1_model"]["generator"] = "wgan" + assert cfg["stage1_model"]["generator"] == "flow" # deep-copied, not aliased + + +def test_migrate_config_empty_dict_still_injects_hardcoded_v02_facts(): + # No [train]/[model] at all still counts as "v0.2" (config_version + # absent) — the hardcoded architectural facts fire unconditionally. + new = gconfig.migrate_config({}) + assert "train" not in new + assert new["conditioning"]["out_dim"] == 128 + assert new["conditioning"]["particle"]["n_layers"] == 2 + assert new["conditioning"]["material"]["n_layers"] == 2 + assert new["stage1_model"]["active"] is True + assert new["stage1_model"]["flow"]["time_dim"] == 64 + assert new["stage1_model"]["ddpm"]["time_dim"] == 64 + assert new["stage2_model"]["active"] is True + assert new["stage2_model"]["flow"]["time_dim"] == 64 + assert new["stage2_model"]["ddpm"]["time_dim"] == 64 + assert new["stage2_model"]["context_dim"] == 64 + assert new["stage2_model"]["decoder"] == "one_shot" + assert new["stage2_model"]["particle_type"]["target"] == "physical" + assert new["meta"] == {"config_version": 3} + + +def test_migrate_config_mode_maps_to_both_stage_generators(): + new = gconfig.migrate_config({"train": {"mode": "wgan"}}) + assert new["stage1_model"]["generator"] == "wgan" + assert new["stage2_model"]["generator"] == "wgan" + + +def test_migrate_config_lambda_nsec_and_lambda_s2(): + new = gconfig.migrate_config({"train": {"lambda_nsec": 0.2, "lambda_s2": 2.0}}) + assert new["stage2_model"]["n_sec"]["lambda"] == 0.2 + assert new["stage2_model"]["lambda"] == 2.0 + + +def test_migrate_config_wgan_knobs_map_to_both_stages(): + new = gconfig.migrate_config( + {"train": {"n_critic": 3, "gp_weight": 5.0, "critic_lr": 1e-4}} ) + for stage in ("stage1_model", "stage2_model"): + assert new[stage]["wgan"]["n_critic"] == 3 + assert new[stage]["wgan"]["gp_weight"] == 5.0 + assert new[stage]["wgan"]["critic_lr"] == 1e-4 -def test_merge_cli_overrides_applies_file_then_cli(tmp_path, monkeypatch): +def test_migrate_config_model_hidden_dim_n_blocks_dropout_map_to_both_stages(): + new = gconfig.migrate_config( + {"model": {"hidden_dim": 128, "n_blocks": 4, "dropout": 0.2}} + ) + for stage in ("stage1_model", "stage2_model"): + assert new[stage]["hidden_dim"] == 128 + assert new[stage]["n_res_blocks"] == 4 + assert new[stage]["dropout"] == 0.2 + + +def test_migrate_config_emb_dim_and_conditioning_map_to_both_axes(): + new = gconfig.migrate_config( + {"model": {"emb_dim": 32, "conditioning": "embedding"}} + ) + for axis in ("particle", "material"): + assert new["conditioning"][axis]["emb_dim"] == 32 + assert new["conditioning"][axis]["type"] == "embedding" + + +def test_migrate_config_noise_dim_maps_to_both_stages_wgan(): + new = gconfig.migrate_config({"model": {"noise_dim": 128}}) + assert new["stage1_model"]["wgan"]["noise_dim"] == 128 + assert new["stage2_model"]["wgan"]["noise_dim"] == 128 + + +def test_migrate_config_k_max_maps_to_stage2_only(): + new = gconfig.migrate_config({"model": {"k_max": 20}}) + assert new["stage2_model"]["k_max"] == 20 + assert "k_max" not in new.get("stage1_model", {}) + + +def test_migrate_config_router_copied_to_both_stages_with_tie_to_stage1_false(): + new = gconfig.migrate_config( + { + "model": { + "router": { + "enabled": True, + "type": "energy", + "n_experts": 10, + "temperature": 0.05, + } + } + } + ) + assert new["stage1_model"]["router"] == { + "enabled": True, + "type": "energy", + "n_experts": 10, + "temperature": 0.05, + } + assert new["stage2_model"]["router"] == { + "enabled": True, + "type": "energy", + "n_experts": 10, + "temperature": 0.05, + "tie_to_stage1": False, + } + + +def test_migrate_config_router_nonzero_expert_dims_raises(): + cfg = { + "model": { + "router": {"enabled": True, "expert_hidden_dim": 128, "expert_n_blocks": 0} + } + } + try: + gconfig.migrate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "expert_hidden_dim" in str(e) + + +def test_migrate_config_router_zero_expert_dims_dropped_silently(): + new = gconfig.migrate_config( + { + "model": { + "router": { + "enabled": True, + "type": "energy", + "n_experts": 4, + "expert_hidden_dim": 0, + "expert_n_blocks": 0, + } + } + } + ) + assert "expert_hidden_dim" not in new["stage1_model"]["router"] + assert "expert_n_blocks" not in new["stage1_model"]["router"] + + +def test_migrate_config_train_passthrough_is_exact(): + new = gconfig.migrate_config({"train": {"epochs": 7, "batch_size": 999, "seed": 3}}) + assert new["train"] == {"epochs": 7, "batch_size": 999, "seed": 3} + + +def test_migrate_config_preserves_meta_git_hash(): + new = gconfig.migrate_config({"meta": {"git_hash": "abc123"}}) + assert new["meta"] == {"git_hash": "abc123", "config_version": 3} + + +def test_migrate_config_real_router_fixture_raises_on_nonzero_expert_dims(): + cfg = gconfig.load_toml(_CONFIGS_DIR / "router_energy_n10_embedding.toml") + try: + gconfig.migrate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "expert_hidden_dim" in str(e) + + +def test_migrate_config_real_wgan_fixture(): + cfg = gconfig.load_toml(_CONFIGS_DIR / "wgan_h128_b4_physical.toml") + new = gconfig.migrate_config(cfg) + assert new["stage1_model"]["generator"] == "wgan" + assert new["stage2_model"]["generator"] == "wgan" + for stage in ("stage1_model", "stage2_model"): + assert new[stage]["hidden_dim"] == 128 + assert new[stage]["n_res_blocks"] == 4 + assert new[stage]["dropout"] == 0.0 + assert new["conditioning"]["particle"]["type"] == "physical" + assert new["conditioning"]["material"]["type"] == "physical" + assert new["train"]["epochs"] == 30 + assert new["train"]["warmup_epochs"] == 3 + + +# --------------------------------------------------------------------------- +# merge_cli_overrides +# --------------------------------------------------------------------------- + + +def test_merge_cli_overrides_defaults_only_matches_default_config(): + cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, None, {}) + assert cfg == gconfig.DEFAULT_CONFIG + assert cfg is not gconfig.DEFAULT_CONFIG + + +def test_merge_cli_overrides_nested_override_keeps_siblings(): + cfg = gconfig.merge_cli_overrides( + gconfig.DEFAULT_CONFIG, + None, + {"stage1_model": {"router": {"enabled": True}}}, + ) + assert cfg["stage1_model"]["router"]["enabled"] is True + assert cfg["stage1_model"]["router"]["type"] == "energy" # default preserved + assert cfg["stage1_model"]["hidden_dim"] == 256 # untouched sibling section + + +def test_merge_cli_overrides_file_then_explicit_override_precedence( + tmp_path, monkeypatch +): monkeypatch.setattr(gconfig, "git_hash", lambda: "abc123") path = tmp_path / "config.toml" - _write_config(path, "abc123") + _write_toml( + path, + git_hash="abc123", + extra="[train]\nepochs = 5\n\n[model]\nhidden_dim = 64\n", + ) cfg = gconfig.merge_cli_overrides( gconfig.DEFAULT_CONFIG, path, - train_overrides={}, - model_overrides={"hidden_dim": 128}, + {"stage1_model": {"hidden_dim": 128}}, ) assert cfg["train"]["epochs"] == 5 # from file - assert cfg["model"]["hidden_dim"] == 128 # CLI override wins over file + assert cfg["stage1_model"]["hidden_dim"] == 128 # explicit override wins over file + assert cfg["stage2_model"]["hidden_dim"] == 64 # migrated from file, not overridden + + +def test_merge_cli_overrides_migrates_v2_file_transparently(tmp_path, monkeypatch): + monkeypatch.setattr(gconfig, "git_hash", lambda: "abc123") + path = tmp_path / "config.toml" + _write_toml( + path, + git_hash="abc123", + extra='[train]\nmode = "wgan"\n\n[model]\nconditioning = "embedding"\n', + ) + + cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) + assert cfg["stage1_model"]["generator"] == "wgan" + assert cfg["stage2_model"]["generator"] == "wgan" + assert cfg["conditioning"]["particle"]["type"] == "embedding" + # hardcoded v0.2 fact still applied even though it's not a CLI-settable key + assert cfg["conditioning"]["particle"]["n_layers"] == 2 def test_merge_cli_overrides_warns_on_git_hash_mismatch(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "current999") path = tmp_path / "config.toml" - _write_config(path, "old111") + _write_toml(path, git_hash="old111", extra="[train]\nepochs = 5\n") - cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {}) - - assert cfg["train"]["epochs"] == 5 # does not fail, config still applied + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) captured = capsys.readouterr() assert "warning" in captured.err assert "old111" in captured.err @@ -52,9 +320,9 @@ def test_merge_cli_overrides_no_warning_on_matching_git_hash( ): monkeypatch.setattr(gconfig, "git_hash", lambda: "same123") path = tmp_path / "config.toml" - _write_config(path, "same123") + _write_toml(path, git_hash="same123", extra="[train]\nepochs = 5\n") - gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {}) + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) assert capsys.readouterr().err == "" @@ -63,9 +331,9 @@ def test_merge_cli_overrides_no_warning_when_git_hash_unknown( ): monkeypatch.setattr(gconfig, "git_hash", lambda: "unknown") path = tmp_path / "config.toml" - _write_config(path, "abc123") + _write_toml(path, git_hash="abc123", extra="[train]\nepochs = 5\n") - gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {}) + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) assert capsys.readouterr().err == "" @@ -76,17 +344,295 @@ def test_merge_cli_overrides_no_warning_when_meta_section_absent( path = tmp_path / "config.toml" path.write_text("[train]\nepochs = 5\n") - gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {}) + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) assert capsys.readouterr().err == "" +def test_merge_cli_overrides_real_default_toml_fixture(monkeypatch): + monkeypatch.setattr( + gconfig, "git_hash", lambda: "c3bf3abebfe29a10fe42b9cbafbb3460ab78d243" + ) + cfg = gconfig.merge_cli_overrides( + gconfig.DEFAULT_CONFIG, _CONFIGS_DIR / "default.toml", {} + ) + assert cfg["stage1_model"]["generator"] == "flow" + assert cfg["stage1_model"]["hidden_dim"] == 256 + assert cfg["stage2_model"]["hidden_dim"] == 256 + assert cfg["conditioning"]["particle"]["emb_dim"] == 16 + assert cfg["conditioning"]["particle"]["n_layers"] == 2 # migrated hardcoded fact + assert cfg["train"]["epochs"] == 100 + assert cfg["stage2_model"]["decoder"] == "one_shot" + + +# --------------------------------------------------------------------------- +# save_config +# --------------------------------------------------------------------------- + + +def test_save_config_round_trips_multi_level_nesting(tmp_path): + cfg = { + "stage1_model": { + "hidden_dim": 256, + "router": { + "enabled": True, + "type": "energy", + "n_experts": 4, + }, + }, + "train": {"epochs": 100}, + } + meta = {"config_version": 3, "git_hash": "abc123"} + + gconfig.save_config(cfg, tmp_path, meta) + loaded = gconfig.load_toml(tmp_path / "config.toml") + + assert loaded["stage1_model"]["hidden_dim"] == 256 + assert loaded["stage1_model"]["router"] == { + "enabled": True, + "type": "energy", + "n_experts": 4, + } + assert loaded["train"] == {"epochs": 100} + assert loaded["meta"] == meta + + +def test_save_config_round_trips_three_level_nesting(tmp_path): + cfg = { + "stage2_model": { + "decoder": "autoregressive", + "n_sec": {"mode": "head", "lambda": 0.1}, + "router": {"tie_to_stage1": True}, + } + } + gconfig.save_config(cfg, tmp_path, {"config_version": 3}) + loaded = gconfig.load_toml(tmp_path / "config.toml") + + assert loaded["stage2_model"]["decoder"] == "autoregressive" + assert loaded["stage2_model"]["n_sec"] == {"mode": "head", "lambda": 0.1} + assert loaded["stage2_model"]["router"] == {"tie_to_stage1": True} + + +# --------------------------------------------------------------------------- +# default_out_dir_name +# --------------------------------------------------------------------------- + +_NOW = datetime(2026, 7, 29, 14, 30) + + +def _cfg_with(**dotted_overrides): + """Build a full DEFAULT_CONFIG-shaped dict with dotted-path overrides + applied via _deep_merge, e.g. _cfg_with(**{"stage1_model.hidden_dim": 512}).""" + overrides: dict = {} + for dotted, value in dotted_overrides.items(): + gconfig._set_path(overrides, dotted, value) + return gconfig._deep_merge(gconfig.DEFAULT_CONFIG, overrides) + + +def test_default_out_dir_name_all_defaults_is_just_the_timestamp(): + assert ( + gconfig.default_out_dir_name(gconfig.DEFAULT_CONFIG, now=_NOW) + == "20260729_1430" + ) + + +def test_default_out_dir_name_stage1_generator_shown_bare_no_prefix(): + cfg = _cfg_with(**{"stage1_model.generator": "wgan"}) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_wgan" + + +def test_default_out_dir_name_stage2_decoder_shown(): + cfg = _cfg_with(**{"stage2_model.decoder": "one_shot"}) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_dec-one_shot" + + +def test_default_out_dir_name_particle_type_target_shown(): + cfg = _cfg_with(**{"stage2_model.particle_type.target": "physical"}) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_pt-physical" + + +def test_default_out_dir_name_particle_conditioning_embedding_abbreviated(): + cfg = _cfg_with(**{"conditioning.particle.type": "embedding"}) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_cemb" + + +def test_default_out_dir_name_stage1_router_shown_as_unit(): + cfg = _cfg_with( + **{ + "stage1_model.router.enabled": True, + "stage1_model.router.type": "energy", + "stage1_model.router.n_experts": 8, + } + ) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_s1r-energy8" + + +def test_default_out_dir_name_stage2_router_shown_as_unit_distinct_from_stage1(): + cfg = _cfg_with( + **{ + "stage2_model.router.enabled": True, + "stage2_model.router.type": "pdg", + "stage2_model.router.n_experts": 3, + } + ) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_s2r-pdg3" + + +def test_default_out_dir_name_router_disabled_omitted_even_if_subfields_nondefault(): + cfg = _cfg_with( + **{ + "stage1_model.router.enabled": False, + "stage1_model.router.type": "pdg", + "stage1_model.router.n_experts": 8, + } + ) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430" + + +def test_default_out_dir_name_router_gumbel_shown_when_enabled(): + cfg = _cfg_with( + **{ + "stage1_model.router.enabled": True, + "stage1_model.router.type": "energy", + "stage1_model.router.n_experts": 8, + "stage1_model.router.gumbel": True, + } + ) + assert ( + gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_s1r-energy8_s1gum" + ) + + +def test_default_out_dir_name_overflow_caps_and_hashes_remainder(): + cfg = _cfg_with( + **{ + "stage1_model.generator": "wgan", + "stage2_model.generator": "flow", + "stage2_model.decoder": "one_shot", + "stage2_model.autoregressive.history": "attention", + "stage2_model.particle_type.target": "physical", + "stage1_model.router.enabled": True, + "stage1_model.router.type": "energy", + "stage1_model.router.n_experts": 8, + "stage2_model.router.enabled": True, + "stage2_model.router.type": "pdg", + "stage2_model.router.n_experts": 3, + } + ) + name = gconfig.default_out_dir_name(cfg, now=_NOW) + # First 6 by priority: stage1_generator, stage2_generator, stage2_decoder, + # stage2_history, particle_type_target, stage1_router — stage2_router + # overflows into the hash suffix. + assert name.startswith( + "20260729_1430_wgan_s2-flow_dec-one_shot_hist-attention_pt-physical_s1r-energy8_+" + ) + + +def test_default_out_dir_name_overflow_hash_is_deterministic_and_value_sensitive(): + overrides = { + "stage1_model.generator": "wgan", + "stage2_model.generator": "flow", + "stage2_model.decoder": "one_shot", + "stage2_model.autoregressive.history": "attention", + "stage2_model.particle_type.target": "physical", + "stage1_model.router.enabled": True, + "stage1_model.router.type": "energy", + "stage1_model.router.n_experts": 8, + "train.seed": 3, + } + name_a = gconfig.default_out_dir_name(_cfg_with(**overrides), now=_NOW) + name_b = gconfig.default_out_dir_name(_cfg_with(**overrides), now=_NOW) + assert name_a == name_b + + changed = dict(overrides, **{"train.seed": 99}) + name_c = gconfig.default_out_dir_name(_cfg_with(**changed), now=_NOW) + assert name_c != name_a + + +# --------------------------------------------------------------------------- +# validate_config +# --------------------------------------------------------------------------- + + +def test_validate_config_default_config_passes(): + gconfig.validate_config(gconfig.DEFAULT_CONFIG) # must not raise + + +def test_validate_config_embedding_target_requires_embedding_conditioning(): + cfg = _cfg_with( + **{ + "stage2_model.particle_type.target": "embedding", + "conditioning.particle.type": "physical", + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "embedding" in str(e) + + +def test_validate_config_embedding_target_passes_with_embedding_conditioning(): + cfg = _cfg_with( + **{ + "stage2_model.particle_type.target": "embedding", + "conditioning.particle.type": "embedding", + } + ) + gconfig.validate_config(cfg) # must not raise + + +def test_validate_config_pdg_router_incompatible_with_physical_conditioning(): + cfg = _cfg_with( + **{ + "stage1_model.router.enabled": True, + "stage1_model.router.type": "pdg", + "conditioning.particle.type": "physical", + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "pdg" in str(e) + + +def test_validate_config_tie_to_stage1_requires_stage1_active(): + cfg = _cfg_with( + **{ + "stage2_model.router.tie_to_stage1": True, + "stage1_model.active": False, + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "tie_to_stage1" in str(e) + + +def test_validate_config_stop_token_not_implemented(): + cfg = _cfg_with(**{"stage2_model.n_sec.mode": "stop_token"}) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "stop_token" in str(e) + + +# --------------------------------------------------------------------------- +# checkpoint config-mismatch warnings (unchanged surface, still exercised) +# --------------------------------------------------------------------------- + + def test_warn_if_checkpoint_config_mismatch_finds_sibling_toml( tmp_path, monkeypatch, capsys ): monkeypatch.setattr(gconfig, "git_hash", lambda: "current999") ckpt_path = tmp_path / "best.pt" - ckpt_path.write_bytes(b"") # contents irrelevant, only its directory is used - _write_config(tmp_path / "config.toml", "old111") + ckpt_path.write_bytes(b"") + _write_toml( + tmp_path / "config.toml", git_hash="old111", extra="[train]\nepochs = 5\n" + ) gconfig.warn_if_checkpoint_config_mismatch(ckpt_path) @@ -113,202 +659,9 @@ def test_warn_if_checkpoint_config_mismatch_no_warning_when_hashes_match( monkeypatch.setattr(gconfig, "git_hash", lambda: "same123") ckpt_path = tmp_path / "best.pt" ckpt_path.write_bytes(b"") - _write_config(tmp_path / "config.toml", "same123") + _write_toml( + tmp_path / "config.toml", git_hash="same123", extra="[train]\nepochs = 5\n" + ) gconfig.warn_if_checkpoint_config_mismatch(ckpt_path) assert capsys.readouterr().err == "" - - -def test_resolve_expert_dims_default_config_inherits_hidden_dim_and_n_blocks(): - # The unset sentinel (expert_hidden_dim/n_blocks == 0 in DEFAULT_CONFIG) - # is exactly the bug fixed by resolve_expert_dims: it must not silently - # fall back to some other hardcoded default, only to the monolith's own - # hidden_dim/n_blocks, so --hidden-dim/--n-blocks reach the experts too. - router_cfg = dict(gconfig.DEFAULT_CONFIG["model"]["router"]) - assert router_cfg["expert_hidden_dim"] == 0 - assert router_cfg["expert_n_blocks"] == 0 - - hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6) - assert (hidden_dim, n_blocks) == (512, 6) - - -def test_resolve_expert_dims_missing_keys_also_inherit(): - hidden_dim, n_blocks = gconfig.resolve_expert_dims({}, 512, 6) - assert (hidden_dim, n_blocks) == (512, 6) - - -def test_default_config_gumbel_router_defaults_off(): - # Straight-through Gumbel-softmax combine weights (giant.model.network. - # Router.combine_weights) must be opt-in — existing routed configs and - # checkpoints should be unaffected unless gumbel is explicitly enabled. - router_cfg = gconfig.DEFAULT_CONFIG["model"]["router"] - assert router_cfg["gumbel"] is False - assert router_cfg["gumbel_tau_start"] == 1.0 - assert router_cfg["gumbel_tau_end"] == 0.1 - - -def test_resolve_expert_dims_explicit_override_wins(): - router_cfg = {"expert_hidden_dim": 128, "expert_n_blocks": 3} - hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6) - assert (hidden_dim, n_blocks) == (128, 3) - - -def test_resolve_expert_dims_partial_override_mixes_explicit_and_inherited(): - router_cfg = {"expert_hidden_dim": 128, "expert_n_blocks": 0} - hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6) - assert (hidden_dim, n_blocks) == (128, 6) - - -def _default_cfg(**overrides): - train_overrides = { - k: v for k, v in overrides.items() if k in gconfig.DEFAULT_CONFIG["train"] - } - model_overrides = { - k: v for k, v in overrides.items() if k in gconfig.DEFAULT_CONFIG["model"] - } - router_overrides = overrides.get("router") - if router_overrides: - model_overrides["router"] = router_overrides - return gconfig.merge_cli_overrides( - gconfig.DEFAULT_CONFIG, None, train_overrides, model_overrides - ) - - -_NOW = datetime(2026, 7, 29, 14, 30) - - -def test_default_out_dir_name_all_defaults_is_just_the_timestamp(): - cfg = _default_cfg() - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430" - - -def test_default_out_dir_name_single_non_default_field(): - cfg = _default_cfg(hidden_dim=512) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_h512" - - -def test_default_out_dir_name_conditioning_embedding_shown_abbreviated(): - cfg = _default_cfg(conditioning="embedding") - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_cemb" - - -def test_default_out_dir_name_conditioning_default_omitted(): - cfg = _default_cfg(conditioning="physical") - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430" - - -def test_default_out_dir_name_router_enabled_shown_as_unit(): - cfg = _default_cfg(router={"enabled": True, "type": "energy", "n_experts": 8}) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_r-energy8" - - -def test_default_out_dir_name_router_disabled_omitted_even_if_subfields_nondefault(): - cfg = _default_cfg(router={"enabled": False, "type": "pdg", "n_experts": 8}) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430" - - -def test_default_out_dir_name_router_gumbel_shown_when_enabled(): - cfg = _default_cfg( - router={"enabled": True, "type": "energy", "n_experts": 8, "gumbel": True} - ) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_r-energy8_gum" - - -def test_default_out_dir_name_router_gumbel_omitted_when_router_disabled(): - cfg = _default_cfg( - router={"enabled": False, "type": "energy", "n_experts": 8, "gumbel": True} - ) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430" - - -def test_default_out_dir_name_router_learn_centers_shown_only_when_disabled(): - cfg_default = _default_cfg( - router={"enabled": True, "type": "energy", "n_experts": 8} - ) - assert ( - gconfig.default_out_dir_name(cfg_default, now=_NOW) == "20260729_1430_r-energy8" - ) - - cfg_off = _default_cfg( - router={ - "enabled": True, - "type": "energy", - "n_experts": 8, - "learn_centers": False, - } - ) - assert ( - gconfig.default_out_dir_name(cfg_off, now=_NOW) - == "20260729_1430_r-energy8_nolc" - ) - - -def test_default_out_dir_name_router_learn_width_and_temperature_shown(): - cfg = _default_cfg( - router={ - "enabled": True, - "type": "energy", - "n_experts": 8, - "learn_width": True, - } - ) - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_r-energy8_lw" - - cfg2 = _default_cfg( - router={ - "enabled": True, - "type": "energy", - "n_experts": 8, - "learn_temperature": True, - } - ) - assert gconfig.default_out_dir_name(cfg2, now=_NOW) == "20260729_1430_r-energy8_lt" - - -def test_default_out_dir_name_mode_shown_bare_no_prefix(): - cfg = _default_cfg(mode="wgan") - assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_wgan" - - -def test_default_out_dir_name_overflow_caps_and_hashes_remainder(): - cfg = _default_cfg( - mode="wgan", - router={"enabled": True, "type": "energy", "n_experts": 8}, - conditioning="embedding", - hidden_dim=512, - n_blocks=8, - emb_dim=32, - lr=1e-3, - batch_size=2048, - seed=3, - epochs=200, - ) - name = gconfig.default_out_dir_name(cfg, now=_NOW) - # First 6 by priority: mode, router, conditioning, hidden_dim, n_blocks, emb_dim. - assert name.startswith("20260729_1430_wgan_r-energy8_cemb_h512_b8_e32_+4more-") - digest = name.split("-")[-1] - assert len(digest) == 6 - - -def test_default_out_dir_name_overflow_hash_is_deterministic_and_value_sensitive(): - base = dict( - mode="wgan", - router={"enabled": True, "type": "energy", "n_experts": 8}, - conditioning="embedding", - hidden_dim=512, - n_blocks=8, - emb_dim=32, - lr=1e-3, - batch_size=2048, - seed=3, - epochs=200, - ) - name_a = gconfig.default_out_dir_name(_default_cfg(**base), now=_NOW) - name_b = gconfig.default_out_dir_name(_default_cfg(**base), now=_NOW) - assert name_a == name_b # stable across calls with the same overflow set - - changed = dict(base, epochs=999) - name_c = gconfig.default_out_dir_name(_default_cfg(**changed), now=_NOW) - assert ( - name_c != name_a - ) # differs when an overflowed value changes # n_blocks inherited, hidden_dim not -- 2.39.5 From 9ce55e501319b5650f461c8b7070be0355835c76 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 10:55:29 +0200 Subject: [PATCH 05/30] v0.3.0 step 2: network.py refactor to composable stage models MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Decomposes the ten permutation classes in giant/model/network.py into the reusable parts from docs/v0.3.0-design.md §5: ConditionEncoder (now independently configurable per particle/material axis), ContextAdapter, Trunk/MonolithicTrunk/RoutedTrunk/ExpertTrunk, and the stage classes Stage1Model/Stage2OneShot/CriticModel (Stage2Autoregressive stubbed, raises NotImplementedError until step 4/5). build_models/build_critics now return a dict keyed by stage and accept the new nested config shape, with routed WGAN reachable for the first time (the old --mode wgan --router rejection is gone) and stage2_model.router.tie_to_stage1 sharing a literal Router instance. A v0.2 checkpoint's flat model_config auto-migrates via _migrate_legacy_model_config + migrate_legacy_state_dict, preserving the n_sec_head's attachment to Stage1Model (legacy_owner="stage1", design doc §4.1). tests/test_migration_v02_v03.py proves this bit-identical against a frozen v0.2 snapshot (tests/legacy/network_v02_snapshot.py) for both flow and wgan, both conditioning modes. scripts/check_migration_v02_v03.py is the real-checkpoint counterpart for a portal machine with /ceph access. giant/model/schedule.py's flow-matching/DDPM loss helpers are updated to the new model-call convention (t as a keyword). giant/sample.py, giant/rollout.py, and giant/validate.py are not yet updated (deferred to design doc step 6) — their exercising tests are marked xfail with that reasoning rather than silently broken. Co-Authored-By: Claude Sonnet 5 --- giant/model/network.py | 1692 ++++++++++++-------------- giant/model/schedule.py | 6 +- scripts/check_migration_v02_v03.py | 174 +++ tests/legacy/__init__.py | 0 tests/legacy/network_v02_snapshot.py | 1104 +++++++++++++++++ tests/test_flow.py | 25 +- tests/test_migration_v02_v03.py | 241 ++++ tests/test_network.py | 40 +- tests/test_phase2.py | 47 +- tests/test_rollout.py | 34 +- tests/test_router.py | 371 +++--- tests/test_validate.py | 36 +- tests/test_wgan.py | 55 +- 13 files changed, 2733 insertions(+), 1092 deletions(-) create mode 100644 scripts/check_migration_v02_v03.py create mode 100644 tests/legacy/__init__.py create mode 100644 tests/legacy/network_v02_snapshot.py create mode 100644 tests/test_migration_v02_v03.py diff --git a/giant/model/network.py b/giant/model/network.py index 85d145f..88e89e9 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -15,9 +15,14 @@ from giant.constants import ( MATERIAL_PHYS_DIM, PARTICLE_PHYS_DIM, SEC_DIM, + SEC_SLOT_DIM, X_DIM, ) +# --------------------------------------------------------------------------- +# Building blocks (docs/v0.3.0-design.md §5.2/§5.3) +# --------------------------------------------------------------------------- + class SinusoidalEmbedding(nn.Module): def __init__(self, dim: int) -> None: @@ -37,75 +42,136 @@ class SinusoidalEmbedding(nn.Module): return torch.cat([args.sin(), args.cos()], dim=-1) # (B, dim) +def _make_axis_mlp(in_dim: int, emb_dim: int, n_layers: int) -> nn.Sequential: + """`n_layers`-deep MLP producing an `emb_dim`-wide vector from `in_dim` + physical properties (`conditioning.{particle,material}.n_layers`). + + `n_layers=1` (the v0.3.0 default): a single `Linear`, no hidden + activation. `n_layers=2` reproduces v0.2's hardcoded depth exactly — + `Linear -> SiLU -> Linear` — which is why `migrate_config` back-fills + `n_layers=2` for migrated configs rather than the v0.3 default of 1 (see + its docstring). + """ + if n_layers < 1: + raise ValueError(f"n_layers must be >= 1, got {n_layers}") + if n_layers == 1: + return nn.Sequential(nn.Linear(in_dim, emb_dim)) + layers: list[nn.Module] = [nn.Linear(in_dim, emb_dim), nn.SiLU()] + for _ in range(n_layers - 2): + layers += [nn.Linear(emb_dim, emb_dim), nn.SiLU()] + layers.append(nn.Linear(emb_dim, emb_dim)) + return nn.Sequential(*layers) + + class ConditionEncoder(nn.Module): """Fuses continuous conditioning with particle/material identity. - Two mutually exclusive ways to turn (pdg, material) identity into the - two `emb_dim`-wide vectors concatenated with the base continuous - conditioning before the fusion MLP: - - "embedding": a learned `nn.Embedding` lookup table per axis, indexed - by `cond_cat`'s dense training-vocab index. Memorizes the training - menu; the original Phase-2 design. - - "physical": a small MLP per axis, mapping the axis's raw physical + The particle and material axes are configured independently + (`particle_cfg`/`material_cfg`, each `{"type", "emb_dim", "n_layers"}` — + see docs/v0.3.0-design.md §3.1) and may mix freely, e.g. material + "physical" with particle "embedding". Three modes per axis: + - "embedding": a learned `nn.Embedding` lookup, indexed by `cond_cat`'s + dense training-vocab index. Memorizes the training menu. + - "physical": an `n_layers`-deep MLP over the axis's raw physical properties (already present in `cond_cont[:, COND_DIM_BASE:]` — see - giant.data.transforms.build_features) to an `emb_dim`-wide vector — - a drop-in replacement for the embedding lookup, computable for any - PDG code / material name rather than only ones seen in training. - Both modes produce the same `in_dim = COND_DIM_BASE + 2*emb_dim` for the - fusion MLP, so only how the two vectors are produced differs. + giant.data.transforms.build_features), computable for any PDG code / + material name rather than only ones seen in training. + - "onehot": not yet implemented (v0.3.0 step 4 — the top-N map isn't + built yet); raises `NotImplementedError` if selected. """ def __init__( self, pdg_vocab: int, mat_vocab: int, + particle_cfg: dict, + material_cfg: dict, cont_dim: int = COND_DIM, - emb_dim: int = 16, out_dim: int = 128, - conditioning: str = "embedding", ) -> None: super().__init__() - if conditioning not in ("embedding", "physical"): - raise ValueError(f"unknown conditioning mode {conditioning!r}") - self.conditioning = conditioning - if conditioning == "embedding": - self.pdg_emb = nn.Embedding(pdg_vocab, emb_dim) - self.mat_emb = nn.Embedding(mat_vocab, emb_dim) - else: - self.particle_mlp = nn.Sequential( - nn.Linear(PARTICLE_PHYS_DIM, emb_dim), - nn.SiLU(), - nn.Linear(emb_dim, emb_dim), + self.particle_cfg = dict(particle_cfg) + self.material_cfg = dict(material_cfg) + + p_type = particle_cfg["type"] + p_emb_dim = particle_cfg["emb_dim"] + if p_type == "embedding": + self.pdg_emb = nn.Embedding(pdg_vocab, p_emb_dim) + elif p_type == "physical": + self.particle_mlp = _make_axis_mlp( + PARTICLE_PHYS_DIM, p_emb_dim, particle_cfg.get("n_layers", 1) ) - self.material_mlp = nn.Sequential( - nn.Linear(MATERIAL_PHYS_DIM, emb_dim), - nn.SiLU(), - nn.Linear(emb_dim, emb_dim), + elif p_type != "onehot": + raise ValueError(f"unknown conditioning.particle.type {p_type!r}") + + m_type = material_cfg["type"] + m_emb_dim = material_cfg["emb_dim"] + if m_type == "embedding": + self.mat_emb = nn.Embedding(mat_vocab, m_emb_dim) + elif m_type == "physical": + self.material_mlp = _make_axis_mlp( + MATERIAL_PHYS_DIM, m_emb_dim, material_cfg.get("n_layers", 1) ) - in_dim = COND_DIM_BASE + 2 * emb_dim + elif m_type != "onehot": + raise ValueError(f"unknown conditioning.material.type {m_type!r}") + + in_dim = COND_DIM_BASE + p_emb_dim + m_emb_dim self.mlp = nn.Sequential( nn.Linear(in_dim, out_dim), nn.SiLU(), nn.Linear(out_dim, out_dim), ) - def forward(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: - if self.conditioning == "embedding": - pdg_e = self.pdg_emb(cond_cat[:, 0]) - mat_e = self.mat_emb(cond_cat[:, 1]) - else: + def _particle_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor): + p_type = self.particle_cfg["type"] + if p_type == "embedding": + return self.pdg_emb(cond_cat[:, 0]) + if p_type == "physical": particle_phys = cond_cont[ :, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM ] + return self.particle_mlp(particle_phys) + raise NotImplementedError( + "conditioning.particle.type='onehot' needs the top-N PDG map " + "(v0.3.0 step 4, not yet implemented)" + ) + + def _material_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor): + m_type = self.material_cfg["type"] + if m_type == "embedding": + return self.mat_emb(cond_cat[:, 1]) + if m_type == "physical": material_phys = cond_cont[:, COND_DIM_BASE + PARTICLE_PHYS_DIM :] - pdg_e = self.particle_mlp(particle_phys) - mat_e = self.material_mlp(material_phys) + return self.material_mlp(material_phys) + raise NotImplementedError( + "conditioning.material.type='onehot' needs the top-N material " + "map (v0.3.0 step 4, not yet implemented)" + ) + + def forward(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + pdg_e = self._particle_embed(cond_cont, cond_cat) + mat_e = self._material_embed(cond_cont, cond_cat) x = torch.cat([cond_cont[:, :COND_DIM_BASE], pdg_e, mat_e], dim=-1) return self.mlp(x) +class ContextAdapter(nn.Module): + """Projects a stage's outcome (e.g. Stage 1's 9D target) down to a + fixed-width context vector for a downstream stage's conditioning — + `stage2_model.context_dim`. Was `SecondaryConditionEncoder.stage1_proj` + (+ its `tanh`) in v0.2; pulled out as its own module in v0.3.0 since + `SecondaryConditionEncoder` as a wrapper class disappears (design doc §5.2).""" + + def __init__(self, in_dim: int, context_dim: int) -> None: + super().__init__() + self.proj = nn.Linear(in_dim, context_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.tanh(self.proj(x)) + + class ResBlock(nn.Module): - def __init__(self, dim: int, cond_dim: int, dropout: float = 0.1) -> None: + def __init__(self, dim: int, cond_dim: int, dropout: float = 0.0) -> None: super().__init__() self.norm = nn.LayerNorm(dim) self.linear1 = nn.Linear(dim, dim) @@ -123,406 +189,9 @@ class ResBlock(nn.Module): return x + h -class DenoisingMLP(nn.Module): - """Stage-1 model: predicts the 9D primary post-step vector field + n_sec logits. - - The n_sec head runs on the condition encoding only (no diffusion noise), - so it can be called at inference time independently via `predict_n_sec`. - """ - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - time_dim: int = 64, - cond_out_dim: int = 128, - x_dim: int = X_DIM, - dropout: float = 0.1, - k_max: int = K_MAX, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.time_emb = SinusoidalEmbedding(time_dim) - self.cond_enc = ConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - merged_cond_dim = time_dim + cond_out_dim - self.input_proj = nn.Linear(x_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_proj = nn.Linear(hidden_dim, x_dim) - # Predicts n_sec as classification over {0, 1, ..., k_max}. - # Applied to the condition encoding (not the diffused latent). - self.n_sec_head = nn.Sequential( - nn.Linear(cond_out_dim, hidden_dim // 2), - nn.SiLU(), - nn.Linear(hidden_dim // 2, k_max + 1), - ) - - def forward( - self, - x_t: torch.Tensor, - t: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - ) -> torch.Tensor: - t_emb = self.time_emb(t) # (B, time_dim) - c_emb = self.cond_enc(cond_cont, cond_cat) # (B, cond_out_dim) - cond = torch.cat([t_emb, c_emb], dim=-1) - x = self.input_proj(x_t) - for block in self.blocks: - x = block(x, cond) - return self.out_proj(x) - - def predict_n_sec( - self, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - ) -> torch.Tensor: - """Return n_sec logits (B, K_MAX+1) from conditioning alone.""" - c_emb = self.cond_enc(cond_cont, cond_cat) - return self.n_sec_head(c_emb) - - -class SecondaryConditionEncoder(nn.Module): - """Encodes pre-step conditioning + Stage-1 output for the secondary decoder.""" - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - emb_dim: int = 16, - cond_out_dim: int = 128, - stage1_dim: int = X_DIM, - stage1_proj_dim: int = 64, - out_dim: int = 128, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.base = ConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - self.stage1_proj = nn.Linear(stage1_dim, stage1_proj_dim) - fused_dim = cond_out_dim + stage1_proj_dim - self.fuse = nn.Sequential( - nn.Linear(fused_dim, out_dim), - nn.SiLU(), - ) - - def forward( - self, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - stage1_out: torch.Tensor, - ) -> torch.Tensor: - base = self.base(cond_cont, cond_cat) # (B, cond_out_dim) - s1 = self.stage1_proj(stage1_out).tanh() # (B, stage1_proj_dim) - return self.fuse(torch.cat([base, s1], dim=-1)) # (B, out_dim) - - -class SecondaryDecoder(nn.Module): - """Stage-2 model: predicts vector field over K_MAX secondary slots simultaneously. - - Each slot encodes (stick_break_logit, local_dir_3D, log_mass, charge) for - one secondary ordered by descending energy — mass/charge are the - secondary's predicted physical identity, regressed directly against real - physics targets (see giant.data.transforms.encode_secondaries), used - as-is with no snapping to a discrete PDG code. Padded slots are masked - from loss. - """ - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - time_dim: int = 64, - cond_out_dim: int = 128, - stage1_proj_dim: int = 64, - sec_dim: int = SEC_DIM, - dropout: float = 0.1, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.time_emb = SinusoidalEmbedding(time_dim) - self.cond_enc = SecondaryConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - cond_out_dim=cond_out_dim, - stage1_proj_dim=stage1_proj_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - merged_cond_dim = time_dim + cond_out_dim - self.input_proj = nn.Linear(sec_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_proj = nn.Linear(hidden_dim, sec_dim) - - def forward( - self, - x_t: torch.Tensor, - t: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - stage1_out: torch.Tensor, - ) -> torch.Tensor: - t_emb = self.time_emb(t) - c_emb = self.cond_enc(cond_cont, cond_cat, stage1_out) - cond = torch.cat([t_emb, c_emb], dim=-1) - x = self.input_proj(x_t) - for block in self.blocks: - x = block(x, cond) - return self.out_proj(x) - - -class WGANGenerator(nn.Module): - """Stage-1 WGAN-GP generator: single forward pass, no diffusion/flow time. - - Same `ConditionEncoder` + `ResBlock` trunk as `DenoisingMLP`, but the - input is a noise vector `z` (not a diffused/interpolated `x_t`) and the - ResBlocks condition on the condition encoding alone (no time embedding to - concatenate) — see `giant/model/wgan.py` for the adversarial losses, and - `giant.sample.sample_wgan` for single-pass sampling. - """ - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - cond_out_dim: int = 128, - x_dim: int = X_DIM, - noise_dim: int = 64, - dropout: float = 0.1, - k_max: int = K_MAX, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.noise_dim = noise_dim - self.cond_enc = ConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - self.input_proj = nn.Linear(noise_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_proj = nn.Linear(hidden_dim, x_dim) - self.n_sec_head = nn.Sequential( - nn.Linear(cond_out_dim, hidden_dim // 2), - nn.SiLU(), - nn.Linear(hidden_dim // 2, k_max + 1), - ) - - def forward( - self, - z: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - ) -> torch.Tensor: - cond = self.cond_enc(cond_cont, cond_cat) - x = self.input_proj(z) - for block in self.blocks: - x = block(x, cond) - return self.out_proj(x) - - def predict_n_sec( - self, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - ) -> torch.Tensor: - """Return n_sec logits (B, K_MAX+1) from conditioning alone.""" - c_emb = self.cond_enc(cond_cont, cond_cat) - return self.n_sec_head(c_emb) - - -class Critic(nn.Module): - """Stage-1 WGAN-GP critic: scalar realism score, own `ConditionEncoder`. - - Kept structurally parallel to `WGANGenerator` (own condition encoder — - separate weights from the generator's, standard GAN practice) but has no - n_sec head: n_sec is never adversarial, it stays a plain classifier on - the generator side. - """ - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - cond_out_dim: int = 128, - x_dim: int = X_DIM, - dropout: float = 0.1, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.cond_enc = ConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - self.input_proj = nn.Linear(x_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_norm = nn.LayerNorm(hidden_dim) - self.out_proj = nn.Linear(hidden_dim, 1) - - def forward( - self, - x: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - ) -> torch.Tensor: - cond = self.cond_enc(cond_cont, cond_cat) - h = self.input_proj(x) - for block in self.blocks: - h = block(h, cond) - return self.out_proj(self.out_norm(h)).squeeze(-1) - - -class WGANSecondaryGenerator(nn.Module): - """Stage-2 WGAN-GP generator: single forward pass over all K_MAX slots. - - Mirrors `SecondaryDecoder` minus the time embedding, the same way - `WGANGenerator` mirrors `DenoisingMLP` — takes noise `z` instead of `x_t`, - conditions on `SecondaryConditionEncoder`'s output alone. - """ - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - cond_out_dim: int = 128, - stage1_proj_dim: int = 64, - sec_dim: int = SEC_DIM, - noise_dim: int = 64, - dropout: float = 0.1, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.noise_dim = noise_dim - self.cond_enc = SecondaryConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - cond_out_dim=cond_out_dim, - stage1_proj_dim=stage1_proj_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - self.input_proj = nn.Linear(noise_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_proj = nn.Linear(hidden_dim, sec_dim) - - def forward( - self, - z: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - stage1_out: torch.Tensor, - ) -> torch.Tensor: - cond = self.cond_enc(cond_cont, cond_cat, stage1_out) - x = self.input_proj(z) - for block in self.blocks: - x = block(x, cond) - return self.out_proj(x) - - -class SecondaryCritic(nn.Module): - """Stage-2 WGAN-GP critic: scalar realism score over the flattened 90D slots.""" - - def __init__( - self, - pdg_vocab: int, - mat_vocab: int, - hidden_dim: int = 256, - n_blocks: int = 6, - emb_dim: int = 16, - cond_out_dim: int = 128, - stage1_proj_dim: int = 64, - sec_dim: int = SEC_DIM, - dropout: float = 0.1, - conditioning: str = "embedding", - ) -> None: - super().__init__() - self.cond_enc = SecondaryConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - cond_out_dim=cond_out_dim, - stage1_proj_dim=stage1_proj_dim, - out_dim=cond_out_dim, - conditioning=conditioning, - ) - self.input_proj = nn.Linear(sec_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) - self.out_norm = nn.LayerNorm(hidden_dim) - self.out_proj = nn.Linear(hidden_dim, 1) - - def forward( - self, - x: torch.Tensor, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - stage1_out: torch.Tensor, - ) -> torch.Tensor: - cond = self.cond_enc(cond_cont, cond_cat, stage1_out) - h = self.input_proj(x) - for block in self.blocks: - h = block(h, cond) - return self.out_proj(self.out_norm(h)).squeeze(-1) +# --------------------------------------------------------------------------- +# Routers — carried over unchanged from v0.2 (docs/v0.3.0-design.md §5.3) +# --------------------------------------------------------------------------- class Router(nn.Module): @@ -537,13 +206,6 @@ class Router(nn.Module): def __init__(self, n_experts: int) -> None: super().__init__() self.n_experts = n_experts - # Opt-in straight-through Gumbel-softmax combine weights (see - # combine_weights below) — off by default, set from model.router.gumbel - # by _build_router_from_cfg. gumbel_tau is annealed per training step - # by giant.train (model.router.gumbel_tau_start/_end); neither is an - # nn.Parameter/buffer since neither is learned or needs checkpointing — - # the tau schedule is deterministic in global_step, so it recomputes - # correctly on resume. self.gumbel = False self.gumbel_tau = 1.0 @@ -556,23 +218,10 @@ class Router(nn.Module): ) -> torch.Tensor: """(B, n_experts) train-time expert-combination weights. - Default (`gumbel=False`): identical to `gate()` — the original dense - soft-mixture combination. Opt-in straight-through Gumbel-softmax - (`gumbel=True`, train mode only): samples a Gumbel-perturbed - categorical draw from the same distribution `gate()` defines - (`log(gate())` is a valid unnormalized-logit input to - `F.gumbel_softmax` since softmax is shift-invariant, so no subclass - needs to expose separate pre-softmax logits), then hardens it to a - one-hot vector on the forward pass while keeping the soft sample's - gradient on the backward pass. This makes the training-time forward - combination match eval-time top-1 dispatch exactly (one expert's - output, unweighted) instead of the smooth blend `gate()` gives — - intended to close the train/eval mismatch identified as a likely - cause of experts overlapping instead of partitioning (see the - router_gating write-up referenced in CLAUDE.md's roadmap). - `gate()` itself is untouched and still backs `balance_loss`/ - `entropy_loss`/`gate_stats`, so those diagnostics keep reading the - smooth distribution rather than a noisy sample. + Default (`gumbel=False`): identical to `gate()`. Opt-in + straight-through Gumbel-softmax (`gumbel=True`, train mode only): + hardens the forward pass to a one-hot sample (matching eval-time + top-1 dispatch) while keeping the soft sample's gradient on backward. """ probs = self.gate(cond_cont, cond_cat) if not (self.gumbel and self.training): @@ -596,51 +245,23 @@ class Router(nn.Module): ) -> torch.Tensor: """Optional supervised auxiliary loss shaping the router's own belief. - Default: none (a scalar 0), for routers like EnergyRouter that read a - quantity directly off cond_cont/cond_cat and need no label. Routers - gating on an unobservable pre-step quantity (e.g. ProcessRouter, - which predicts the physics process that will end the step) override - this to supervise their internal classifier against the true label. + Default: none (a scalar 0). Routers gating on an unobservable + pre-step quantity (e.g. ProcessRouter) override this. """ return torch.zeros((), device=cond_cont.device) def entropy_loss( self, cond_cont: torch.Tensor, cond_cat: torch.Tensor ) -> torch.Tensor: - """Optional auxiliary loss rewarding sharper (lower-entropy) routing. - - Reuses `gate_stats`'s `norm_entropy` (already in [0, 1], 1.0 = - uniform/collapsed) directly as the loss, so minimizing it pushes - every router's gate toward decisiveness. A generic base-class - default — works for any Router via gate_stats, no per-subclass - override needed. Off by default (see `lambda_entropy` in - giant.train): bounded width/temperature (EnergyRouter's - `learn_width`/`learn_temperature`) is the primary defense against - gate collapse; this is a secondary, use-with-caution lever, since - indiscriminately penalizing entropy can also suppress legitimate - soft ambiguity near a router's own decision boundary. - """ + """Optional auxiliary loss rewarding sharper (lower-entropy) routing.""" norm_entropy, _ = self.gate_stats(cond_cont, cond_cat) return norm_entropy def gate_stats( self, cond_cont: torch.Tensor, cond_cat: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: - """Diagnostics for catching a router that fails to specialize. - - Returns `(norm_entropy, importance)`: - - `norm_entropy`: scalar, the batch-mean of each row's gate entropy - divided by `log(n_experts)`, in [0, 1] and comparable across - routers with different `n_experts` (1.0 = uniform/collapsed - gating, 0.0 = fully hard routing). - - `importance`: (n_experts,) tensor, `gate(...).sum(dim=0)` — the - *unnormalized* per-expert weight mass for this batch. Callers - wanting a global utilization share across many batches must sum - this across batches first and normalize once at the end; - averaging per-batch shares instead would treat every batch as - equally important regardless of size and understate a - rarely-but-fully-used expert. - """ + """Diagnostics: `(norm_entropy, importance)` — see v0.2 docstring for + the full explanation, unchanged in v0.3.0.""" gate = self.gate(cond_cont, cond_cat) # (B, n_experts) row_entropy = -(gate * (gate + 1e-8).log()).sum(dim=-1) # (B,) norm_entropy = row_entropy.mean() / math.log(self.n_experts) @@ -662,10 +283,10 @@ def register_router(name: str): def build_router(name: str, n_experts: int, **kwargs) -> Router: """Factory: look up a `Router` subclass by name from the registry. - Every registered router type is fed the same `model.router` config - dict; kwargs not declared by that type's constructor are silently - dropped, so per-type hyperparameters (e.g. EnergyRouter's - `temperature`) can coexist in one config without special-casing. + Every registered router type is fed the same `router` config dict; + kwargs not declared by that type's constructor are silently dropped, so + per-type hyperparameters (e.g. EnergyRouter's `temperature`) can coexist + in one config without special-casing. """ if name not in ROUTER_REGISTRY: raise ValueError( @@ -679,16 +300,13 @@ def build_router(name: str, n_experts: int, **kwargs) -> Router: def _bounded_interp(raw: torch.Tensor, lo: float, hi: float) -> torch.Tensor: """Sigmoid interpolation into `[lo, hi]` — smooth, always-positive-gradient - bound (unlike `clamp`, which zeroes gradient past the boundary) used for - EnergyRouter's `learn_width`/`learn_temperature` modes.""" + bound used for EnergyRouter's `learn_width`/`learn_temperature` modes.""" return lo + (hi - lo) * torch.sigmoid(raw) def _inverse_bounded_interp(value: float, lo: float, hi: float) -> float: """Inverse of `_bounded_interp`, used once at construction to warm-start - `raw` so `_bounded_interp(raw, lo, hi) == value` — lets `learn_width`/ - `learn_temperature` start out exactly reproducing the fixed-`temperature` - gate before any training moves them.""" + `raw` so the initial effective width/temperature exactly equals `value`.""" p = min(max((value - lo) / (hi - lo), 1e-6), 1 - 1e-6) return math.log(p / (1 - p)) @@ -697,37 +315,9 @@ def _inverse_bounded_interp(value: float, lo: float, hi: float) -> float: class EnergyRouter(Router): """Soft turn-on gate over normalized pre-step log-energy. - Reads `cond_cont[:, energy_idx]` (ignores cond_cat). Learnable (or - fixed) 1-D centers. By default initialized spread evenly across - [-2, 2] — an assumed-uniform z-normalized energy range that may not - match the true (often skewed) distribution and can leave experts - overlapping instead of partitioning the range; pass `centers_init` to - seed them from data (e.g. energy quantiles) instead. - `gate(e) = softmax_i(-(e - c_i)^2 / tau)`, differentiable in e; as - tau -> 0 this hardens to nearest-center (Voronoi) selection, which is - exactly what `top1` uses at eval. - - `temperature` is normally a single fixed scalar shared by every expert. - Two mutually exclusive optional modes generalize it: - - `learn_width`: each expert gets its own learnable width, so - `gate(e) = softmax_i(-(e - c_i)^2 / width_i)` — experts can learn - independently how much of the energy axis they cover. - - `learn_temperature`: the single shared `temperature` itself becomes - learnable (still one scalar for every expert). - Both parameterize their raw learnable value through a sigmoid bounded - into `[width_min_ratio, width_max_ratio] * temperature` (see - `_bounded_interp`), warm-started so the initial effective width/ - temperature exactly equals `temperature` — enabling either mode is a - no-op at init. The bound is deliberately not raw `softplus`/`exp` - (unbounded above): an unbounded width lets one expert's width run away - to infinity, making its logit `-d2/width -> 0` almost everywhere so it - wins nearly every row regardless of true distance to its center — the - same "experts overlap instead of partitioning" failure this whole - router design is trying to avoid, just via a new mechanism. See - `Router.entropy_loss`/`giant.train`'s `lambda_entropy` for a secondary, - optional guard against all experts' widths co-inflating together - (which bounding caps but doesn't forbid, and which the load-balance - loss alone can't see since usage shares stay even throughout). + Reads `cond_cont[:, energy_idx]` (ignores cond_cat). `gate(e) = + softmax_i(-(e - c_i)^2 / tau)`; as tau -> 0 this hardens to + nearest-center (Voronoi) selection, exactly what `top1` uses at eval. """ def __init__( @@ -777,9 +367,6 @@ class EnergyRouter(Router): self.register_buffer("centers", centers) def effective_width(self) -> torch.Tensor | float: - """Softmax denominator used by `gate()`: a fixed scalar `temperature` - (default), a per-expert `(n_experts,)` bounded width (`learn_width`), - or a single bounded learnable scalar (`learn_temperature`).""" if self.learn_width: return _bounded_interp(self.raw_width, self._width_lo, self._width_hi) if self.learn_temperature: @@ -794,20 +381,9 @@ class EnergyRouter(Router): @register_router("pdg") class PdgRouter(Router): - """Soft turn-on gate over a learned PDG embedding. - - Unlike ProcessRouter's process label, PDG code is already known at - pre-step time (it's a conditioning input, `cond_cat[:, 0]`), so no - supervision is needed — `classify_loss` falls back to the Router base - class's zero-loss default, same as EnergyRouter. Because PDG is - categorical rather than a scalar, this generalizes EnergyRouter's - soft-turn-on-then-Voronoi trick from a 1-D distance to a distance in a - small embedding space: its own embedding table (kept separate from the - trunk's ConditionEncoder, same reasoning as ProcessRouter's own - pdg/mat embeddings) maps each PDG code to a point, and `n_experts` - learnable (or fixed) centers partition that space. - `gate(pdg) = softmax_i(-||emb(pdg) - c_i||^2 / tau)`. - """ + """Soft turn-on gate over a learned PDG embedding (own table, separate + from the trunk's `ConditionEncoder`). No supervision needed — PDG code + is already known at pre-step time.""" def __init__( self, @@ -836,28 +412,12 @@ class PdgRouter(Router): @register_router("process") class ProcessRouter(Router): - """Routes on the physics process expected to end the step. - - Unlike EnergyRouter (which reads a quantity that's already known at - pre-step time), the process — Compton, photoelectric, brems, ... — is a - *post-step outcome*: it can't be read off cond_cont/cond_cat directly. - Instead this router runs a small classifier over pre-step conditioning - (its own pdg/material embeddings, kept separate from the trunk's - ConditionEncoder) that predicts it, one class per expert slot - (`n_experts` doubles as the number of process classes — see - `build_process_map_from_files`, which caps the process vocabulary to - exactly this many classes, bucketing rare processes into a shared - "other" slot). - - The classifier is supervised by `classify_loss` against the true - `process` label (see `giant/train.py`) — a *training-time* signal only; - `gate`/`top1` never see it, so eval-time dispatch (rollout, predict) - needs no ground truth, same as every other Router. This sidesteps the - gradient/differentiability problem that sank the earlier - process-conditioned-flow proposal (see the archived decision doc): the - hard categorical choice only ever feeds a non-differentiable expert - *dispatch*, never the flow's own conditioning path. - """ + """Routes on the physics process expected to end the step — a post-step + outcome, so a small classifier over pre-step conditioning predicts it + (own pdg/material embeddings, separate from the trunk's ConditionEncoder). + `n_experts` doubles as the number of process classes. Supervised via + `classify_loss` against the true `process` label at train time only; + `gate`/`top1` never see it.""" def __init__( self, @@ -877,7 +437,6 @@ class ProcessRouter(Router): ) def logits(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: - """(B, n_experts) raw process-classifier logits, one class per expert.""" pdg_e = self.pdg_emb(cond_cat[:, 0]) mat_e = self.mat_emb(cond_cat[:, 1]) h = torch.cat([cond_cont, pdg_e, mat_e], dim=-1) @@ -893,25 +452,8 @@ class ProcessRouter(Router): class ComposedRouter(Router): - """Joint router over independent axes (e.g. energy x pdg), outer-product gated. - - Wraps N already-built sub-routers, each free to have its own - `n_experts` and hyperparameters (an `EnergyRouter(n_experts=4, ...)` - composed with a `PdgRouter(n_experts=3, ...)` needs no axis to match - the other's expert count). The joint gate is the outer product of the - per-axis softmax gates, flattened to `(B, prod(n_experts_i))` — still a - partition of unity, since each factor is one. Because the axes are - routed independently, the joint argmax factors into the per-axis - argmaxes, so `top1` (inherited from `Router`) costs no more than - routing each axis alone despite the multiplicative expert count; the - same is true of `balance_loss` (inherited, computed on the flattened - joint gate — now one importance term per *joint* expert cell). - - Not registered in `ROUTER_REGISTRY` / buildable via `build_router`, - since those assume one `n_experts` int shared by a single router type; - use `build_composed_router` instead, which resolves a list of per-axis - specs (each independently typed and sized) through `build_router`. - """ + """Joint router over independent axes (e.g. energy x pdg), outer-product + gated. Not registered in `ROUTER_REGISTRY`; use `build_composed_router`.""" def __init__(self, routers: list[Router]) -> None: if not routers: @@ -934,7 +476,6 @@ class ComposedRouter(Router): def classify_loss( self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor ) -> torch.Tensor: - """Sum of each sub-router's own classify_loss (0 for unsupervised axes).""" total = torch.zeros((), device=cond_cont.device) for router in self.routers: total = total + router.classify_loss(cond_cont, cond_cat, labels) @@ -942,15 +483,8 @@ class ComposedRouter(Router): def build_composed_router(specs: list[dict], **shared_kwargs) -> ComposedRouter: - """Build a `ComposedRouter` from a list of per-axis router specs. - - Each spec is a `{"type": ..., "n_experts": ..., ...per-axis kwargs}` - dict resolved through `build_router` exactly like a single-axis router - config, so axes can differ in both expert count and hyperparameters - (e.g. an energy axis's `temperature` vs a pdg axis's `emb_dim`). - `shared_kwargs` (`pdg_vocab`, `mat_vocab`, ...) are merged under each - spec, with the spec's own keys taking precedence. - """ + """Build a `ComposedRouter` from a list of per-axis router specs — see + `_parse_composed_axes`.""" routers = [ build_router( spec["type"], @@ -965,30 +499,116 @@ def build_composed_router(specs: list[dict], **shared_kwargs) -> ComposedRouter: return ComposedRouter(routers) +_AXIS_KEY_RE = re.compile(r"^axis(\d+)_(.+)$") + + +def _parse_composed_axes(router_cfg: dict) -> list[dict]: + """Regroup `axis{i}_{field}` flat keys into a list of per-axis spec dicts. + + e.g. `axis0_type = "energy"`, `axis0_n_experts = 4`, `axis1_type = "pdg"`, + `axis1_n_experts = 3`, `axis1_emb_dim = 8`. Axis indices must be + contiguous from 0. + """ + axes: dict[int, dict] = {} + for key, value in router_cfg.items(): + m = _AXIS_KEY_RE.match(key) + if m is None: + continue + idx, field = int(m.group(1)), m.group(2) + axes.setdefault(idx, {})[field] = value + missing = set(range(len(axes))) - axes.keys() + if missing: + raise ValueError(f"composed router config has gaps at axis indices {missing}") + return [axes[i] for i in range(len(axes))] + + +# Router types that read cond_cat's pdg index through their own +# nn.Embedding(pdg_vocab, ...), regardless of the trunk's particle +# conditioning mode — see _check_router_conditioning_compat. +_VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process") + + +def _check_router_conditioning_compat( + router_types: list[str], particle_conditioning: str +) -> None: + """Reject a router axis that reintroduces a training-vocab PDG lookup + under `conditioning.particle.type = "physical"`. + + `PdgRouter`/`ProcessRouter` always build their own dataset-scoped + `nn.Embedding(pdg_vocab, ...)`, independent of `ConditionEncoder`'s + particle mode. Pairing either with `"physical"` would silently + reintroduce a training-menu-scoped lookup at the routing layer, + defeating the point of physical-property conditioning. Raised loudly at + model-build time. + """ + bad = sorted(set(router_types) & set(_VOCAB_SCOPED_ROUTER_TYPES)) + if bad and particle_conditioning == "physical": + raise ValueError( + f"router type(s) {bad} always use a training-vocab PDG embedding, " + "which is incompatible with conditioning.particle.type='physical' " + "(whose whole point is generalizing beyond that vocab) — pick a " + "different router type (e.g. 'energy') or use " + "conditioning.particle.type='embedding'." + ) + + +def _build_router_from_cfg( + router_cfg: dict, + pdg_vocab: int, + mat_vocab: int, + particle_conditioning: str = "embedding", +) -> Router: + """Resolve one stage's `router` config into a `Router`, single-axis or + composed. `gumbel` is set as a post-construction attribute (shared by + every router type, not a per-type constructor kwarg).""" + shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab) + if router_cfg["type"] == "composed": + axes = _parse_composed_axes(router_cfg) + _check_router_conditioning_compat( + [a["type"] for a in axes], particle_conditioning + ) + router = build_composed_router(axes, **shared_vocab) + router.gumbel = bool(router_cfg.get("gumbel", False)) + return router + _check_router_conditioning_compat([router_cfg["type"]], particle_conditioning) + router_kwargs = { + k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts") + } + router_kwargs.setdefault("pdg_vocab", pdg_vocab) + router_kwargs.setdefault("mat_vocab", mat_vocab) + router = build_router(router_cfg["type"], router_cfg["n_experts"], **router_kwargs) + router.gumbel = bool(router_cfg.get("gumbel", False)) + return router + + +# --------------------------------------------------------------------------- +# Trunks (docs/v0.3.0-design.md §5.2 (b)) +# --------------------------------------------------------------------------- + + class ExpertTrunk(nn.Module): """One small expert: `input_proj -> ResBlock stack -> out_proj`. - Same shape as the monolithic DenoisingMLP/SecondaryDecoder trunk, but - intended to be narrower/shallower (per-call cost is the whole point). + Unlike v0.2, `out_dim` is independent of `in_dim` — needed by stage-2 AR + tokens later (`noise_dim` in, `4 + type_dim` out), even though every + step-2/3 caller still has `in_dim == out_dim`. """ def __init__( self, in_dim: int, + out_dim: int, hidden_dim: int, n_blocks: int, - merged_cond_dim: int, - dropout: float = 0.1, + cond_dim: int, + dropout: float = 0.0, ) -> None: super().__init__() self.input_proj = nn.Linear(in_dim, hidden_dim) self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] + [ResBlock(hidden_dim, cond_dim, dropout=dropout) for _ in range(n_blocks)] ) - self.out_proj = nn.Linear(hidden_dim, in_dim) + self.out_proj = nn.Linear(hidden_dim, out_dim) def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: x = self.input_proj(x) @@ -1006,26 +626,23 @@ def _route_forward( cond_cat: torch.Tensor, training: bool, ) -> torch.Tensor: - """Shared dispatch for both Routed* trunks. + """Shared dispatch for `RoutedTrunk`. Train mode: full mixture `sum_i weight_i * expert_i(x)` — always - N-expert dense compute, fully differentiable. `weight` is - `router.combine_weights(...)`: the plain soft `gate()` by default, or (see - `Router.combine_weights`) a straight-through Gumbel-softmax one-hot sample - when `router.gumbel` is enabled — either way, no change to the compute - cost of this branch. Eval mode: grouped top-1 dispatch — each row runs - exactly one (small) expert, which is the actual source of the per-call - speedup this architecture is for. + N-expert dense compute, fully differentiable (`weight` is + `router.combine_weights`). Eval mode: grouped top-1 dispatch — each row + runs exactly one expert, the actual source of the per-call speedup. """ if training: weights = router.combine_weights(cond_cont, cond_cat) # (B, n_experts) - out = torch.zeros_like(x) + out = torch.zeros(x.shape[0], experts[0].out_proj.out_features, device=x.device) for i, expert in enumerate(experts): out = out + weights[:, i : i + 1] * expert(x, cond) return out idx = router.top1(cond_cont, cond_cat) # (B,) - out = torch.zeros_like(x) + out_dim = experts[0].out_proj.out_features + out = torch.zeros(x.shape[0], out_dim, device=x.device) for i, expert in enumerate(experts): mask = idx == i if mask.any(): @@ -1033,349 +650,632 @@ def _route_forward( return out -class RoutedDenoisingMLP(nn.Module): - """Routed drop-in for `DenoisingMLP`. +class Trunk(nn.Module): + """Interface implemented by `MonolithicTrunk`/`RoutedTrunk`: everything + downstream of the fused conditioning vector, i.e. the actual generative + trunk of a stage (`input_proj -> blocks -> out_proj`, monolithic or + expert-routed).""" - Shares the time embedding, `ConditionEncoder`, and `n_sec_head` (all - tiny) across experts and routes only the trunk (where the FLOPs are). - Same `forward`/`predict_n_sec` signatures as `DenoisingMLP`, so - sample.py/rollout.py/validate.py need no changes. + def forward( + self, + x: torch.Tensor, + cond: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + raise NotImplementedError + + +class MonolithicTrunk(Trunk): + def __init__( + self, + in_dim: int, + out_dim: int, + hidden_dim: int, + n_res_blocks: int, + cond_dim: int, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.input_proj = nn.Linear(in_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, cond_dim, dropout=dropout) + for _ in range(n_res_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, out_dim) + + def forward( + self, + x: torch.Tensor, + cond: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + x = self.input_proj(x) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + +class RoutedTrunk(Trunk): + def __init__( + self, + router: Router, + in_dim: int, + out_dim: int, + hidden_dim: int, + n_res_blocks: int, + cond_dim: int, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.router = router + self.experts = nn.ModuleList( + [ + ExpertTrunk( + in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout + ) + for _ in range(router.n_experts) + ] + ) + + def forward( + self, + x: torch.Tensor, + cond: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + return _route_forward( + self.experts, self.router, x, cond, cond_cont, cond_cat, self.training + ) + + +def build_trunk( + router: Router | None, + in_dim: int, + out_dim: int, + hidden_dim: int, + n_res_blocks: int, + cond_dim: int, + dropout: float = 0.0, +) -> Trunk: + if router is not None: + return RoutedTrunk( + router, in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout + ) + return MonolithicTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) + + +# --------------------------------------------------------------------------- +# Stage models (docs/v0.3.0-design.md §5.3) +# --------------------------------------------------------------------------- + + +class Stage1Model(nn.Module): + """Predicts the 9D primary post-step vector. No `n_sec_head` — decision 1 + (docs/v0.3.0-design.md §2) moves it to stage 2, except for a migrated + v0.2 checkpoint (`n_sec_head_k_max` given), where it stays attached here + since that's where its weights live and what conditioning it was trained + against (see `_migrate_legacy_model_config`).""" + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + particle_cfg: dict, + material_cfg: dict, + hidden_dim: int = 256, + n_res_blocks: int = 6, + cond_out_dim: int = 128, + x_dim: int = X_DIM, + dropout: float = 0.0, + generator: str = "flow", + time_dim: int = 64, + noise_dim: int = 64, + router: Router | None = None, + n_sec_head_k_max: int | None = None, + ) -> None: + super().__init__() + self.generator_kind = generator + self.noise_dim = noise_dim + self.cond_enc = ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + ) + has_time = generator in ("flow", "ddpm") + self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None + merged_cond_dim = (time_dim if has_time else 0) + cond_out_dim + in_dim = noise_dim if generator == "wgan" else x_dim + self.trunk = build_trunk( + router, in_dim, x_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout + ) + self.n_sec_head = None + if n_sec_head_k_max is not None: + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, n_sec_head_k_max + 1), + ) + + def forward( + self, + x_t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + t: torch.Tensor | None = None, + ) -> torch.Tensor: + c_emb = self.cond_enc(cond_cont, cond_cat) + cond = ( + torch.cat([self.time_emb(t), c_emb], dim=-1) + if self.time_emb is not None + else c_emb + ) + return self.trunk(x_t, cond, cond_cont, cond_cat) + + def predict_n_sec( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor + ) -> torch.Tensor: + """Return n_sec logits (B, K_MAX+1) from conditioning alone. Only + valid on a migrated v0.2 checkpoint's Stage1Model — fresh v0.3.0 + configs predict n_sec from Stage2OneShot instead (decision 1).""" + if self.n_sec_head is None: + raise RuntimeError( + "this Stage1Model has no n_sec_head — n_sec now lives on " + "stage 2 by default (decision 1); this method only exists " + "for a migrated v0.2 checkpoint (legacy_owner='stage1')" + ) + c_emb = self.cond_enc(cond_cont, cond_cat) + return self.n_sec_head(c_emb) + + +class Stage2OneShot(nn.Module): + """Predicts all `k_max` secondary slots simultaneously — v0.2 behaviour, + reproduced exactly (see docs/v0.3.0-design.md §12 step 2 acceptance + criterion; `decoder = "autoregressive"` is `Stage2Autoregressive`, + step 4/5, not implemented yet). + + Owns `n_sec_head` by default (decision 1) unless `build_n_sec_head=False` + (a migrated v0.2 checkpoint, whose n_sec_head instead attaches to + Stage1Model — see `_migrate_legacy_model_config`). """ def __init__( self, pdg_vocab: int, mat_vocab: int, - router: Router, - expert_hidden_dim: int = 128, - expert_n_blocks: int = 3, - emb_dim: int = EMB_DIM, - time_dim: int = 64, + particle_cfg: dict, + material_cfg: dict, + hidden_dim: int = 256, + n_res_blocks: int = 6, cond_out_dim: int = 128, + context_dim: int = 64, + sec_dim: int = SEC_DIM, x_dim: int = X_DIM, - dropout: float = 0.1, + dropout: float = 0.0, + generator: str = "wgan", + time_dim: int = 64, + noise_dim: int = 64, k_max: int = K_MAX, - conditioning: str = "embedding", + router: Router | None = None, + build_n_sec_head: bool = True, ) -> None: super().__init__() - self.router = router - self.time_emb = SinusoidalEmbedding(time_dim) + self.generator_kind = generator + self.noise_dim = noise_dim self.cond_enc = ConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - out_dim=cond_out_dim, - conditioning=conditioning, + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim ) - merged_cond_dim = time_dim + cond_out_dim - self.experts = nn.ModuleList( - [ - ExpertTrunk( - x_dim, - expert_hidden_dim, - expert_n_blocks, - merged_cond_dim, - dropout=dropout, - ) - for _ in range(router.n_experts) - ] - ) - self.n_sec_head = nn.Sequential( - nn.Linear(cond_out_dim, cond_out_dim), + self.context_adapter = ContextAdapter(x_dim, context_dim) + self.fuse = nn.Sequential( + nn.Linear(cond_out_dim + context_dim, cond_out_dim), nn.SiLU(), - nn.Linear(cond_out_dim, k_max + 1), ) + has_time = generator in ("flow", "ddpm") + self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None + merged_cond_dim = (time_dim if has_time else 0) + cond_out_dim + in_dim = noise_dim if generator == "wgan" else sec_dim + self.trunk = build_trunk( + router, in_dim, sec_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout + ) + self.n_sec_head = None + if build_n_sec_head: + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, k_max + 1), + ) + + def _cond_embed( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor + ) -> torch.Tensor: + base = self.cond_enc(cond_cont, cond_cat) + ctx = self.context_adapter(stage1_out) + return self.fuse(torch.cat([base, ctx], dim=-1)) def forward( self, x_t: torch.Tensor, - t: torch.Tensor, cond_cont: torch.Tensor, cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + t: torch.Tensor | None = None, ) -> torch.Tensor: - t_emb = self.time_emb(t) - c_emb = self.cond_enc(cond_cont, cond_cat) - cond = torch.cat([t_emb, c_emb], dim=-1) - return _route_forward( - self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training + c_emb = self._cond_embed(cond_cont, cond_cat, stage1_out) + cond = ( + torch.cat([self.time_emb(t), c_emb], dim=-1) + if self.time_emb is not None + else c_emb ) + return self.trunk(x_t, cond, cond_cont, cond_cat) def predict_n_sec( self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, + stage1_out: torch.Tensor, ) -> torch.Tensor: - """Return n_sec logits (B, K_MAX+1) from conditioning alone.""" - c_emb = self.cond_enc(cond_cont, cond_cat) + if self.n_sec_head is None: + raise RuntimeError( + "this Stage2OneShot has no n_sec_head — it belongs to a " + "migrated v0.2 checkpoint (legacy_owner='stage1'); call " + "stage1.predict_n_sec(cond_cont, cond_cat) instead" + ) + c_emb = self._cond_embed(cond_cont, cond_cat, stage1_out) return self.n_sec_head(c_emb) -class RoutedSecondaryDecoder(nn.Module): - """Routed drop-in for `SecondaryDecoder`. +class Stage2Autoregressive(nn.Module): + """Not implemented until v0.3.0 steps 4-7 (docs/v0.3.0-design.md §6, §12) + — stub so `stage2_model.decoder = "autoregressive"` (the v0.3.0 default) + fails loudly instead of silently no-op-ing.""" - Shares the time embedding and `SecondaryConditionEncoder` across - experts and routes only the trunk. Same `forward` signature as - `SecondaryDecoder`. - """ + def __init__(self, *args, **kwargs) -> None: + super().__init__() + raise NotImplementedError( + "stage2_model.decoder = 'autoregressive' is not implemented yet " + "(design doc v0.3.0 steps 4-7) — use decoder = 'one_shot' for now" + ) + + +class CriticModel(nn.Module): + """Generator-agnostic WGAN-GP critic body: a scalar realism score, for + either stage (`stage="stage1"` mirrors v0.2 `Critic`; `stage="stage2"` + mirrors v0.2 `SecondaryCritic`, adding the same context-fusion path as + `Stage2OneShot`). Used only when that stage's `generator == "wgan"`.""" def __init__( self, pdg_vocab: int, mat_vocab: int, - router: Router, - expert_hidden_dim: int = 128, - expert_n_blocks: int = 3, - emb_dim: int = EMB_DIM, - time_dim: int = 64, + particle_cfg: dict, + material_cfg: dict, + in_dim: int, + hidden_dim: int = 256, + n_res_blocks: int = 6, cond_out_dim: int = 128, - stage1_proj_dim: int = 64, - sec_dim: int = SEC_DIM, - dropout: float = 0.1, - conditioning: str = "embedding", + dropout: float = 0.0, + stage: str = "stage1", + context_dim: int = 64, + context_in_dim: int = X_DIM, ) -> None: super().__init__() - self.router = router - self.time_emb = SinusoidalEmbedding(time_dim) - self.cond_enc = SecondaryConditionEncoder( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - emb_dim=emb_dim, - cond_out_dim=cond_out_dim, - stage1_proj_dim=stage1_proj_dim, - out_dim=cond_out_dim, - conditioning=conditioning, + if stage not in ("stage1", "stage2"): + raise ValueError(f"stage must be 'stage1' or 'stage2', got {stage!r}") + self.stage = stage + self.cond_enc = ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim ) - merged_cond_dim = time_dim + cond_out_dim - self.experts = nn.ModuleList( + if stage == "stage2": + self.context_adapter = ContextAdapter(context_in_dim, context_dim) + self.fuse = nn.Sequential( + nn.Linear(cond_out_dim + context_dim, cond_out_dim), + nn.SiLU(), + ) + self.input_proj = nn.Linear(in_dim, hidden_dim) + self.blocks = nn.ModuleList( [ - ExpertTrunk( - sec_dim, - expert_hidden_dim, - expert_n_blocks, - merged_cond_dim, - dropout=dropout, - ) - for _ in range(router.n_experts) + ResBlock(hidden_dim, cond_out_dim, dropout=dropout) + for _ in range(n_res_blocks) ] ) + self.out_norm = nn.LayerNorm(hidden_dim) + self.out_proj = nn.Linear(hidden_dim, 1) def forward( self, - x_t: torch.Tensor, - t: torch.Tensor, + x: torch.Tensor, cond_cont: torch.Tensor, cond_cat: torch.Tensor, - stage1_out: torch.Tensor, + stage1_out: torch.Tensor | None = None, ) -> torch.Tensor: - t_emb = self.time_emb(t) - c_emb = self.cond_enc(cond_cont, cond_cat, stage1_out) - cond = torch.cat([t_emb, c_emb], dim=-1) - return _route_forward( - self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training - ) + base = self.cond_enc(cond_cont, cond_cat) + if self.stage == "stage2": + ctx = self.context_adapter(stage1_out) + cond = self.fuse(torch.cat([base, ctx], dim=-1)) + else: + cond = base + h = self.input_proj(x) + for block in self.blocks: + h = block(h, cond) + return self.out_proj(self.out_norm(h)).squeeze(-1) -_STAGE1_MODEL_KEYS = { - "pdg_vocab", - "mat_vocab", - "hidden_dim", - "n_blocks", - "emb_dim", - "dropout", - "k_max", - "conditioning", -} -_SEC_DECODER_MODEL_KEYS = { - "pdg_vocab", - "mat_vocab", - "hidden_dim", - "n_blocks", - "emb_dim", - "dropout", - "conditioning", -} -_WGAN_GENERATOR_MODEL_KEYS = _STAGE1_MODEL_KEYS | {"noise_dim"} -_WGAN_SEC_GENERATOR_MODEL_KEYS = _SEC_DECODER_MODEL_KEYS | {"noise_dim"} -# Critic has no n_sec head (n_sec is never adversarial), so it doesn't accept -# k_max the way DenoisingMLP/WGANGenerator do. -_CRITIC_MODEL_KEYS = _STAGE1_MODEL_KEYS - {"k_max"} +# --------------------------------------------------------------------------- +# v0.2 -> v0.3 checkpoint migration (docs/v0.3.0-design.md §4.1, §4.3) +# --------------------------------------------------------------------------- -_AXIS_KEY_RE = re.compile(r"^axis(\d+)_(.+)$") +def _migrate_legacy_model_config(model_config: dict) -> dict: + """Translate a v0.2 checkpoint's flat `model_config` (giant/pipeline.py's + old shape: `hidden_dim`/`n_blocks`/`emb_dim`/`dropout`/`conditioning`/ + `router`/`mode`/... all at one level) into the nested + `{"pdg_vocab", "mat_vocab", "conditioning", "stage1_model", + "stage2_model"}` shape `build_models` expects. + Sets `stage2_model.n_sec.legacy_owner = "stage1"` so the n_sec_head + weights a v0.2 checkpoint carries on its Stage-1 module keep loading + there (design doc §4.1) instead of the new default location + (`Stage2OneShot`) — the n_sec head was trained against Stage 1's own + `ConditionEncoder` output, so it has to stay attached to Stage 1's + module, not just be labeled as such. -def _parse_composed_axes(router_cfg: dict) -> list[dict]: - """Regroup `axis{i}_{field}` flat keys into a list of per-axis spec dicts. - - Flat keys (rather than a nested list-of-dicts) keep composed-router - config expressible in the same one-level-of-nesting TOML/CLI shape as - every other router option (`model.router` stays a flat table of - scalars) — e.g. `axis0_type = "energy"`, `axis0_n_experts = 4`, - `axis1_type = "pdg"`, `axis1_n_experts = 3`, `axis1_emb_dim = 8`. - Axis indices must be contiguous from 0; order follows the index, not - dict insertion order (TOML/CLI merging doesn't preserve it reliably). + Only the monolithic (non-routed) trunk shape is exercised by the step-2 + migration test (docs/v0.3.0-design.md §4.3); a routed v0.2 checkpoint + still builds correctly here (the router config passes through), but its + state dict isn't covered by `migrate_legacy_state_dict` below. """ - axes: dict[int, dict] = {} - for key, value in router_cfg.items(): - m = _AXIS_KEY_RE.match(key) - if m is None: - continue - idx, field = int(m.group(1)), m.group(2) - axes.setdefault(idx, {})[field] = value - missing = set(range(len(axes))) - axes.keys() - if missing: - raise ValueError(f"composed router config has gaps at axis indices {missing}") - return [axes[i] for i in range(len(axes))] + m = model_config + conditioning_mode = m.get("conditioning", "embedding") + generator = m.get("mode", "flow") + hidden_dim = m.get("hidden_dim", 256) + n_blocks = m.get("n_blocks", 6) + emb_dim = m.get("emb_dim", EMB_DIM) + dropout = m.get("dropout", 0.1) + k_max = m.get("k_max", K_MAX) + noise_dim = m.get("noise_dim", 64) + router_cfg = dict(m.get("router") or {}) + router_cfg.setdefault("enabled", False) - -# Router types that read cond_cat's pdg index through their own -# nn.Embedding(pdg_vocab, ...), regardless of the trunk's `conditioning` -# mode — see _check_router_conditioning_compat. -_VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process") - - -def _check_router_conditioning_compat( - router_types: list[str], conditioning: str -) -> None: - """Reject a router axis that reintroduces a training-vocab PDG lookup - under `conditioning="physical"`. - - `PdgRouter`/`ProcessRouter` always build their own dataset-scoped - `nn.Embedding(pdg_vocab, ...)` (network.py's PdgRouter/ProcessRouter), - independent of `ConditionEncoder`'s `conditioning` mode. Pairing either - with `conditioning="physical"` would silently reintroduce a - training-menu-scoped lookup at the routing layer — defeating the entire - point of physical-property conditioning, which is to generalize to a - species/material outside that menu (see giant/rollout.py's - `build_cond_features(strict=...)` gate for the same concern on the - trunk side). Raised loudly at model-build time rather than left to - surface as a confusing rollout/generalization-benchmark result. - """ - bad = sorted(set(router_types) & set(_VOCAB_SCOPED_ROUTER_TYPES)) - if bad and conditioning == "physical": - raise ValueError( - f"router type(s) {bad} always use a training-vocab PDG embedding, " - "which is incompatible with conditioning='physical' (whose whole " - "point is generalizing beyond that vocab) — pick a different " - "router type (e.g. 'energy') or use conditioning='embedding'." - ) - - -def _build_router_from_cfg( - router_cfg: dict, pdg_vocab: int, mat_vocab: int, conditioning: str = "embedding" -) -> Router: - """Resolve one `model.router` config into a `Router`, single-axis or composed. - - `router_cfg["type"] == "composed"` reads `axis{i}_{field}` flat keys - (see `_parse_composed_axes`) instead of a single `type`/`n_experts` pair. - - `gumbel` is set as a post-construction attribute here rather than a - per-subclass constructor kwarg, same reasoning as `lambda_balance`/ - `lambda_proc`/`lambda_entropy` living in `router_cfg` without being a - `Router` subclass constructor param: it's a training-time toggle shared by - every router type, not a per-type hyperparameter (`build_router`'s - kwarg-filtering would otherwise just silently drop it). - """ - shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab) - if router_cfg["type"] == "composed": - axes = _parse_composed_axes(router_cfg) - _check_router_conditioning_compat([a["type"] for a in axes], conditioning) - router = build_composed_router(axes, **shared_vocab) - router.gumbel = bool(router_cfg.get("gumbel", False)) - return router - _check_router_conditioning_compat([router_cfg["type"]], conditioning) - router_kwargs = { - k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts") + return { + "pdg_vocab": m["pdg_vocab"], + "mat_vocab": m["mat_vocab"], + "conditioning": { + "out_dim": 128, + "share_stages": False, + "particle": {"type": conditioning_mode, "emb_dim": emb_dim, "n_layers": 2}, + "material": {"type": conditioning_mode, "emb_dim": emb_dim, "n_layers": 2}, + }, + "stage1_model": { + "active": True, + "generator": generator, + "hidden_dim": hidden_dim, + "n_res_blocks": n_blocks, + "dropout": dropout, + "flow": {"time_dim": 64}, + "ddpm": {"time_dim": 64}, + "wgan": {"noise_dim": noise_dim}, + "router": dict(router_cfg), + }, + "stage2_model": { + "active": True, + "decoder": "one_shot", + "generator": generator, + "hidden_dim": hidden_dim, + "n_res_blocks": n_blocks, + "dropout": dropout, + "k_max": k_max, + "context_dim": 64, + "n_sec": {"mode": "head", "legacy_owner": "stage1"}, + "particle_type": {"target": "physical"}, + "flow": {"time_dim": 64}, + "ddpm": {"time_dim": 64}, + "wgan": {"noise_dim": noise_dim}, + "router": {**router_cfg, "tie_to_stage1": False}, + }, } - # Not every router needs these (EnergyRouter doesn't declare them, so - # build_router's kwarg filtering drops them silently) but ProcessRouter - # needs its own pdg/material embeddings sized to match the checkpoint's - # vocab, same as the trunk's ConditionEncoder. - router_kwargs.setdefault("pdg_vocab", pdg_vocab) - router_kwargs.setdefault("mat_vocab", mat_vocab) - router = build_router(router_cfg["type"], router_cfg["n_experts"], **router_kwargs) - router.gumbel = bool(router_cfg.get("gumbel", False)) - return router -def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]: - """Construct (stage1, sec_decoder) from a persisted/CLI model_config dict. +def migrate_legacy_state_dict( + old_stage1_sd: dict, old_stage2_sd: dict +) -> tuple[dict, dict]: + """Remap a v0.2 checkpoint's (`DenoisingMLP`-or-`WGANGenerator`, + `SecondaryDecoder`-or-`WGANSecondaryGenerator`) state dicts onto the new + `(Stage1Model, Stage2OneShot)` module structure produced by + `build_models(_migrate_legacy_model_config(model_config))`. - Dispatches to the routed pair when `model_config["router"]["enabled"]` - is truthy; a missing/absent "router" key (pre-routing checkpoints) - falls back to the monolithic pair unchanged, so this is a drop-in - replacement for the ad-hoc constructions it replaces. - - `model_config.get("conditioning", "embedding")` — old checkpoints have no - "conditioning" key and must keep loading with their original embedding - tables, so the default here is "embedding", not the training-time - default (which is "physical" — see giant.config.DEFAULT_CONFIG). Read - once and passed to both stage1/sec_decoder, so they structurally always - share one mode. + Only the monolithic (non-routed) trunk shape is handled — see + docs/v0.3.0-design.md §4.3's migration test scope. """ - if model_config.get("mode") == "wgan": - stage1 = WGANGenerator( - **{k: v for k, v in model_config.items() if k in _WGAN_GENERATOR_MODEL_KEYS} - ) - sec_decoder = WGANSecondaryGenerator( - **{ - k: v - for k, v in model_config.items() - if k in _WGAN_SEC_GENERATOR_MODEL_KEYS - } - ) - return stage1, sec_decoder - router_cfg = model_config.get("router") - if router_cfg and router_cfg.get("enabled"): - pdg_vocab = model_config["pdg_vocab"] - mat_vocab = model_config["mat_vocab"] - shared = dict( + def _trunk_prefix(k: str) -> str: + if k.startswith(("input_proj.", "blocks.", "out_proj.")): + return f"trunk.{k}" + return k + + new_stage1 = {} + for k, v in old_stage1_sd.items(): + if k.startswith("n_sec_head."): + new_stage1[k] = v # stays top-level (legacy_owner="stage1") + else: + new_stage1[_trunk_prefix(k)] = v + + new_stage2 = {} + for k, v in old_stage2_sd.items(): + if k.startswith("cond_enc.base."): + new_stage2["cond_enc." + k[len("cond_enc.base.") :]] = v + elif k.startswith("cond_enc.stage1_proj."): + new_stage2["context_adapter.proj." + k[len("cond_enc.stage1_proj.") :]] = v + elif k.startswith("cond_enc.fuse."): + new_stage2["fuse." + k[len("cond_enc.fuse.") :]] = v + else: + new_stage2[_trunk_prefix(k)] = v + + return new_stage1, new_stage2 + + +# --------------------------------------------------------------------------- +# Factories (docs/v0.3.0-design.md §5.4) +# --------------------------------------------------------------------------- + + +def build_models(model_config: dict) -> dict[str, nn.Module | None]: + """Construct `{"stage1": ..., "stage2": ...}` from a config dict — either + the new nested shape (has a `"stage1_model"` key, plus `"pdg_vocab"`/ + `"mat_vocab"`/`"conditioning"` at the top level) or a v0.2 checkpoint's + flat `model_config`, auto-migrated via `_migrate_legacy_model_config`. + + A stage is `None` in the result when that stage's `active = False`. + `stage2_model.router.tie_to_stage1` shares stage 1's literal `Router` + instance rather than building a second, independently-parameterized one + (v0.2's actual — probably accidental — behaviour: two routers built from + one config with no semantic relationship between them). + """ + cfg = ( + model_config + if "stage1_model" in model_config + else _migrate_legacy_model_config(model_config) + ) + pdg_vocab = cfg["pdg_vocab"] + mat_vocab = cfg["mat_vocab"] + conditioning = cfg["conditioning"] + if conditioning.get("share_stages"): + raise NotImplementedError( + "conditioning.share_stages = true is not implemented yet — each " + "stage always builds its own ConditionEncoder for now" + ) + particle_cfg = conditioning["particle"] + material_cfg = conditioning["material"] + particle_conditioning = particle_cfg["type"] + s1cfg = cfg["stage1_model"] + s2cfg = cfg["stage2_model"] + cond_out_dim = conditioning.get("out_dim", 128) + + result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} + + stage1_router: Router | None = None + if s1cfg.get("active", True): + router_cfg = s1cfg.get("router") or {} + if router_cfg.get("enabled"): + stage1_router = _build_router_from_cfg( + router_cfg, pdg_vocab, mat_vocab, particle_conditioning + ) + generator = s1cfg.get("generator", "flow") + gen_sub = s1cfg.get(generator, {}) or {} + legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") + n_sec_head_k_max = ( + s2cfg.get("k_max", K_MAX) if legacy_owner == "stage1" else None + ) + result["stage1"] = Stage1Model( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, - expert_hidden_dim=model_config.get("expert_hidden_dim") - or model_config.get("hidden_dim", 128), - expert_n_blocks=model_config.get("expert_n_blocks") - or model_config.get("n_blocks", 3), - emb_dim=model_config.get("emb_dim", EMB_DIM), - dropout=model_config.get("dropout", 0.1), - conditioning=model_config.get("conditioning", "embedding"), + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=s1cfg.get("hidden_dim", 256), + n_res_blocks=s1cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + dropout=s1cfg.get("dropout", 0.0), + generator=generator, + time_dim=gen_sub.get("time_dim", 64), + noise_dim=(s1cfg.get("wgan") or {}).get("noise_dim", 64), + router=stage1_router, + n_sec_head_k_max=n_sec_head_k_max, ) - conditioning = shared["conditioning"] - stage1 = RoutedDenoisingMLP( - router=_build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, conditioning - ), - k_max=model_config.get("k_max", K_MAX), - **shared, + + if s2cfg.get("active", True): + decoder = s2cfg.get("decoder", "one_shot") + if decoder == "autoregressive": + result["stage2"] = Stage2Autoregressive() + return result + router_cfg = s2cfg.get("router") or {} + stage2_router: Router | None = None + if router_cfg.get("enabled"): + if router_cfg.get("tie_to_stage1") and stage1_router is not None: + stage2_router = stage1_router + else: + stage2_router = _build_router_from_cfg( + router_cfg, pdg_vocab, mat_vocab, particle_conditioning + ) + generator = s2cfg.get("generator", "wgan") + gen_sub = s2cfg.get(generator, {}) or {} + legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") + k_max = s2cfg.get("k_max", K_MAX) + result["stage2"] = Stage2OneShot( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=s2cfg.get("hidden_dim", 256), + n_res_blocks=s2cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + context_dim=s2cfg.get("context_dim", 64), + sec_dim=k_max * SEC_SLOT_DIM, + dropout=s2cfg.get("dropout", 0.0), + generator=generator, + time_dim=gen_sub.get("time_dim", 64), + noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), + k_max=k_max, + router=stage2_router, + build_n_sec_head=legacy_owner != "stage1", ) - sec_decoder = RoutedSecondaryDecoder( - router=_build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, conditioning - ), - **shared, + + return result + + +def build_critics(model_config: dict) -> dict[str, nn.Module | None]: + """Construct `{"stage1": ..., "stage2": ...}` critics for `generator = + "wgan"` training. Training-only — never persisted for inference the way + `build_models`'s pair is. `None` for a stage that's inactive or not + WGAN.""" + cfg = ( + model_config + if "stage1_model" in model_config + else _migrate_legacy_model_config(model_config) + ) + pdg_vocab = cfg["pdg_vocab"] + mat_vocab = cfg["mat_vocab"] + conditioning = cfg["conditioning"] + particle_cfg = conditioning["particle"] + material_cfg = conditioning["material"] + cond_out_dim = conditioning.get("out_dim", 128) + s1cfg = cfg["stage1_model"] + s2cfg = cfg["stage2_model"] + + result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} + + if s1cfg.get("active", True) and s1cfg.get("generator") == "wgan": + result["stage1"] = CriticModel( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + in_dim=X_DIM, + hidden_dim=s1cfg.get("hidden_dim", 256), + n_res_blocks=s1cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + dropout=s1cfg.get("dropout", 0.0), + stage="stage1", ) - return stage1, sec_decoder - stage1 = DenoisingMLP( - **{k: v for k, v in model_config.items() if k in _STAGE1_MODEL_KEYS} - ) - sec_decoder = SecondaryDecoder( - **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} - ) - return stage1, sec_decoder + if ( + s2cfg.get("active", True) + and s2cfg.get("generator") == "wgan" + and s2cfg.get("decoder", "one_shot") != "autoregressive" + ): + k_max = s2cfg.get("k_max", K_MAX) + result["stage2"] = CriticModel( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + in_dim=k_max * SEC_SLOT_DIM, + hidden_dim=s2cfg.get("hidden_dim", 256), + n_res_blocks=s2cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + dropout=s2cfg.get("dropout", 0.0), + stage="stage2", + context_dim=s2cfg.get("context_dim", 64), + ) - -def build_critics(model_config: dict) -> tuple[nn.Module, nn.Module]: - """Construct (critic, sec_critic) for `--mode wgan` training. - - Training-only — never persisted for inference the way `build_models`'s - pair is, since `predict`/`rollout` only ever run the generators. - """ - critic = Critic( - **{k: v for k, v in model_config.items() if k in _CRITIC_MODEL_KEYS} - ) - sec_critic = SecondaryCritic( - **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} - ) - return critic, sec_critic + return result diff --git a/giant/model/schedule.py b/giant/model/schedule.py index 05d1e18..d9f437a 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -48,7 +48,7 @@ class CosineSchedule: noise = torch.randn_like(x0) x_t = self.q_sample(x0, t, noise) t_norm = t.float() / self.T - pred = model(x_t, t_norm, cond_cont, cond_cat) + pred = model(x_t, cond_cont, cond_cat, t=t_norm) return F.mse_loss(pred, noise) @@ -67,7 +67,7 @@ def flow_matching_loss( x0 = torch.randn_like(x1) x_t = (1.0 - t.view(-1, 1)) * x0 + t.view(-1, 1) * x1 u_t = x1 - x0 - v_t = model(x_t, t, cond_cont, cond_cat) + v_t = model(x_t, cond_cont, cond_cat, t=t) return F.mse_loss(v_t, u_t) @@ -103,7 +103,7 @@ def flow_matching_loss_secondary( x0 = torch.randn_like(x1) x_t = (1.0 - t.view(-1, 1)) * x0 + t.view(-1, 1) * x1 u_t = x1 - x0 - v_t = model(x_t, t, cond_cont, cond_cat, stage1_out) + v_t = model(x_t, cond_cont, cond_cat, stage1_out, t=t) err = ((v_t - u_t) ** 2).view(B, K_MAX, SEC_SLOT_DIM) cont_err = err[..., :CONT_SLOT_DIM].mean(dim=-1) # (B, K_MAX) diff --git a/scripts/check_migration_v02_v03.py b/scripts/check_migration_v02_v03.py new file mode 100644 index 0000000..20ff32e --- /dev/null +++ b/scripts/check_migration_v02_v03.py @@ -0,0 +1,174 @@ +"""Portal-machine follow-up for v0.3.0 step 2 (docs/v0.3.0-design.md §4.3): +diff a real v0.2 checkpoint's outputs against the new `build_models` on the +same input batch. + +`tests/test_migration_v02_v03.py` already proves this bit-identical with +synthetic random weights, but that test can't run where it matters (no +`/ceph` on local dev machines — see CLAUDE.md's Compute environment +section). This script is the real-checkpoint counterpart: run it on a portal +machine against an actual trained checkpoint before merging +`v0.3.0-stage2-autoregressive` to `master`. + +Usage (from the repo root, on a portal machine): + + uv run python scripts/check_migration_v02_v03.py /ceph/lbogner/.../best.pt + uv run python scripts/check_migration_v02_v03.py /ceph/lbogner/.../best.pt --ema + uv run python scripts/check_migration_v02_v03.py /ceph/lbogner/.../best.pt --batch 32 --seed 1 + +Run it once against a flow (or ddpm) checkpoint and once against a wgan +checkpoint (design doc §4.3's "one flow checkpoint and one WGAN checkpoint"). +A routed checkpoint (`model_config["router"]["enabled"]`) is only checked for +successful construction — `giant.model.network.migrate_legacy_state_dict` +doesn't yet remap routed (Expert-per-router) state dicts, so the +bit-identical assertion is skipped with a clear warning in that case (see the +function's own docstring for why). +""" + +import argparse +import sys +from pathlib import Path + +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from giant.constants import COND_DIM, SEC_SLOT_DIM, X_DIM # noqa: E402 +from giant.model import network as net # noqa: E402 +from tests.legacy import network_v02_snapshot as legacy # noqa: E402 + + +def _random_batch(model_config: dict, batch: int, seed: int): + g = torch.Generator().manual_seed(seed) + pdg_vocab = model_config["pdg_vocab"] + mat_vocab = model_config["mat_vocab"] + k_max = model_config.get("k_max", 15) + noise_dim = model_config.get("noise_dim", 64) + + cond_cont = torch.randn(batch, COND_DIM, generator=g) + cond_cat = torch.stack( + [ + torch.randint(0, pdg_vocab, (batch,), generator=g), + torch.randint(0, mat_vocab, (batch,), generator=g), + ], + dim=1, + ) + x1 = torch.randn(batch, X_DIM, generator=g) + x2 = torch.randn(batch, k_max * SEC_SLOT_DIM, generator=g) + t = torch.rand(batch, generator=g) + z1 = torch.randn(batch, noise_dim, generator=g) + z2 = torch.randn(batch, noise_dim, generator=g) + return cond_cont, cond_cat, x1, x2, t, z1, z2 + + +def _max_abs_diff(a: torch.Tensor, b: torch.Tensor) -> float: + return (a - b).abs().max().item() + + +def main() -> int: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("checkpoint", type=Path, help="Path to a v0.2 best.pt/last.pt") + p.add_argument( + "--ema", + action="store_true", + help="Use the checkpoint's EMA weights (model_ema/sec_decoder_ema) — " + "what predict/rollout actually sample from — instead of raw weights.", + ) + p.add_argument("--batch", type=int, default=16) + p.add_argument("--seed", type=int, default=0) + args = p.parse_args() + + ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False) + if "model_config" not in ckpt: + print(f"FAIL: {args.checkpoint} has no 'model_config' key — can't migrate it") + return 1 + model_config = ckpt["model_config"] + mode = model_config.get("mode", "flow") + routed = bool((model_config.get("router") or {}).get("enabled")) + print(f"checkpoint: {args.checkpoint}") + print( + f" mode={mode!r} conditioning={model_config.get('conditioning')!r} " + f"routed={routed} ema={args.ema}" + ) + + stage1_key = "model_ema" if args.ema and "model_ema" in ckpt else "model" + stage2_key = ( + "sec_decoder_ema" if args.ema and "sec_decoder_ema" in ckpt else "sec_decoder" + ) + if args.ema and stage1_key == "model": + print( + " warning: --ema requested but no model_ema in checkpoint, using raw weights" + ) + + # --- old side: the frozen v0.2 snapshot, loaded with the checkpoint's own weights --- + old_stage1, old_stage2 = legacy.build_models(model_config) + old_stage1.load_state_dict(ckpt[stage1_key]) + old_stage2.load_state_dict(ckpt[stage2_key]) + old_stage1.eval() + old_stage2.eval() + + # --- new side: migrated config + remapped state dict, through the new build_models --- + new_models = net.build_models(model_config) + new_stage1, new_stage2 = new_models["stage1"], new_models["stage2"] + assert new_stage1 is not None and new_stage2 is not None + + if routed: + print( + " routed checkpoint: migrate_legacy_state_dict only handles the " + "monolithic trunk shape — verifying construction only, skipping " + "the bit-identical weight/output comparison. See " + "docs/v0.3.0-design.md §2.4's scope note." + ) + print("PASS (construction only, routed checkpoint)") + return 0 + + remapped1, remapped2 = net.migrate_legacy_state_dict( + ckpt[stage1_key], ckpt[stage2_key] + ) + missing1, unexpected1 = new_stage1.load_state_dict(remapped1, strict=True) + missing2, unexpected2 = new_stage2.load_state_dict(remapped2, strict=True) + if missing1 or unexpected1 or missing2 or unexpected2: + print("FAIL: state dict mismatch after remap") + print(f" stage1 missing={missing1} unexpected={unexpected1}") + print(f" stage2 missing={missing2} unexpected={unexpected2}") + return 1 + new_stage1.eval() + new_stage2.eval() + + cond_cont, cond_cat, x1, x2, t, z1, z2 = _random_batch( + model_config, args.batch, args.seed + ) + + ok = True + with torch.no_grad(): + if mode == "wgan": + old_out1 = old_stage1(z1, cond_cont, cond_cat) + new_out1 = new_stage1(z1, cond_cont, cond_cat) + else: + old_out1 = old_stage1(x1, t, cond_cont, cond_cat) + new_out1 = new_stage1(x1, cond_cont, cond_cat, t=t) + old_n_sec = old_stage1.predict_n_sec(cond_cont, cond_cat) + new_n_sec = new_stage1.predict_n_sec(cond_cont, cond_cat) + if mode == "wgan": + old_out2 = old_stage2(z2, cond_cont, cond_cat, old_out1) + new_out2 = new_stage2(z2, cond_cont, cond_cat, new_out1) + else: + old_out2 = old_stage2(x2, t, cond_cont, cond_cat, old_out1) + new_out2 = new_stage2(x2, cond_cont, cond_cat, new_out1, t=t) + + for label, old_out, new_out in [ + ("stage1 output", old_out1, new_out1), + ("n_sec logits", old_n_sec, new_n_sec), + ("stage2 output", old_out2, new_out2), + ]: + identical = torch.equal(old_out, new_out) + diff = _max_abs_diff(old_out, new_out) + status = "OK" if identical else "MISMATCH" + print(f" {label}: {status} (max abs diff = {diff:.3e})") + ok = ok and identical + + print("PASS" if ok else "FAIL") + return 0 if ok else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/legacy/__init__.py b/tests/legacy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/legacy/network_v02_snapshot.py b/tests/legacy/network_v02_snapshot.py new file mode 100644 index 0000000..8d34eb7 --- /dev/null +++ b/tests/legacy/network_v02_snapshot.py @@ -0,0 +1,1104 @@ +"""Frozen snapshot of `giant/model/network.py` as it stood at the v0.3.0 +"step 1" commit (eb6dd27), i.e. the last commit before the step-2 §5 +decomposition (see `docs/v0.3.0-design.md`). + +This is a deliberate verbatim copy, not an import of the live module — the +whole point is that this file's classes keep behaving exactly as v0.2 did +even after `giant/model/network.py` itself is rewritten, so +`tests/test_migration_v02_v03.py` has a stable "old" side to diff the new +`build_models`/`Stage1Model`/`Stage2OneShot` against (design doc §4.3's +bit-identical acceptance test). Do not edit this file to track future +`network.py` changes — it exists specifically to stop tracking them. +""" + +import inspect +import math +import re +from collections.abc import Sequence + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from giant.constants import ( + COND_DIM, + COND_DIM_BASE, + EMB_DIM, + K_MAX, + MATERIAL_PHYS_DIM, + PARTICLE_PHYS_DIM, + SEC_DIM, + X_DIM, +) + + +class SinusoidalEmbedding(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + assert dim % 2 == 0, "dim must be even" + half = dim // 2 + freqs = torch.exp( + -math.log(10000) + * torch.arange(half, dtype=torch.float32) + / max(half - 1, 1) + ) + self.register_buffer("freqs", freqs) + + def forward(self, t: torch.Tensor) -> torch.Tensor: + t = t.reshape(-1, 1).float() + args = t * self.freqs.unsqueeze(0) # (B, half) + return torch.cat([args.sin(), args.cos()], dim=-1) # (B, dim) + + +class ConditionEncoder(nn.Module): + """Fuses continuous conditioning with particle/material identity. + + Two mutually exclusive ways to turn (pdg, material) identity into the + two `emb_dim`-wide vectors concatenated with the base continuous + conditioning before the fusion MLP: + - "embedding": a learned `nn.Embedding` lookup table per axis, indexed + by `cond_cat`'s dense training-vocab index. Memorizes the training + menu; the original Phase-2 design. + - "physical": a small MLP per axis, mapping the axis's raw physical + properties (already present in `cond_cont[:, COND_DIM_BASE:]` — see + giant.data.transforms.build_features) to an `emb_dim`-wide vector — + a drop-in replacement for the embedding lookup, computable for any + PDG code / material name rather than only ones seen in training. + Both modes produce the same `in_dim = COND_DIM_BASE + 2*emb_dim` for the + fusion MLP, so only how the two vectors are produced differs. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + cont_dim: int = COND_DIM, + emb_dim: int = 16, + out_dim: int = 128, + conditioning: str = "embedding", + ) -> None: + super().__init__() + if conditioning not in ("embedding", "physical"): + raise ValueError(f"unknown conditioning mode {conditioning!r}") + self.conditioning = conditioning + if conditioning == "embedding": + self.pdg_emb = nn.Embedding(pdg_vocab, emb_dim) + self.mat_emb = nn.Embedding(mat_vocab, emb_dim) + else: + self.particle_mlp = nn.Sequential( + nn.Linear(PARTICLE_PHYS_DIM, emb_dim), + nn.SiLU(), + nn.Linear(emb_dim, emb_dim), + ) + self.material_mlp = nn.Sequential( + nn.Linear(MATERIAL_PHYS_DIM, emb_dim), + nn.SiLU(), + nn.Linear(emb_dim, emb_dim), + ) + in_dim = COND_DIM_BASE + 2 * emb_dim + self.mlp = nn.Sequential( + nn.Linear(in_dim, out_dim), + nn.SiLU(), + nn.Linear(out_dim, out_dim), + ) + + def forward(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + if self.conditioning == "embedding": + pdg_e = self.pdg_emb(cond_cat[:, 0]) + mat_e = self.mat_emb(cond_cat[:, 1]) + else: + particle_phys = cond_cont[ + :, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM + ] + material_phys = cond_cont[:, COND_DIM_BASE + PARTICLE_PHYS_DIM :] + pdg_e = self.particle_mlp(particle_phys) + mat_e = self.material_mlp(material_phys) + x = torch.cat([cond_cont[:, :COND_DIM_BASE], pdg_e, mat_e], dim=-1) + return self.mlp(x) + + +class ResBlock(nn.Module): + def __init__(self, dim: int, cond_dim: int, dropout: float = 0.1) -> None: + super().__init__() + self.norm = nn.LayerNorm(dim) + self.linear1 = nn.Linear(dim, dim) + self.cond_proj = nn.Linear(cond_dim, dim, bias=False) + self.act = nn.SiLU() + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim, dim) + + def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + h = self.norm(x) + h = self.linear1(h) + self.cond_proj(cond) + h = self.act(h) + h = self.dropout(h) + h = self.linear2(h) + return x + h + + +class DenoisingMLP(nn.Module): + """Stage-1 model: predicts the 9D primary post-step vector field + n_sec logits. + + The n_sec head runs on the condition encoding only (no diffusion noise), + so it can be called at inference time independently via `predict_n_sec`. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + time_dim: int = 64, + cond_out_dim: int = 128, + x_dim: int = X_DIM, + dropout: float = 0.1, + k_max: int = K_MAX, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.time_emb = SinusoidalEmbedding(time_dim) + self.cond_enc = ConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + merged_cond_dim = time_dim + cond_out_dim + self.input_proj = nn.Linear(x_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, x_dim) + # Predicts n_sec as classification over {0, 1, ..., k_max}. + # Applied to the condition encoding (not the diffused latent). + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, k_max + 1), + ) + + def forward( + self, + x_t: torch.Tensor, + t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + t_emb = self.time_emb(t) # (B, time_dim) + c_emb = self.cond_enc(cond_cont, cond_cat) # (B, cond_out_dim) + cond = torch.cat([t_emb, c_emb], dim=-1) + x = self.input_proj(x_t) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + def predict_n_sec( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + """Return n_sec logits (B, K_MAX+1) from conditioning alone.""" + c_emb = self.cond_enc(cond_cont, cond_cat) + return self.n_sec_head(c_emb) + + +class SecondaryConditionEncoder(nn.Module): + """Encodes pre-step conditioning + Stage-1 output for the secondary decoder.""" + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + emb_dim: int = 16, + cond_out_dim: int = 128, + stage1_dim: int = X_DIM, + stage1_proj_dim: int = 64, + out_dim: int = 128, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.base = ConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + self.stage1_proj = nn.Linear(stage1_dim, stage1_proj_dim) + fused_dim = cond_out_dim + stage1_proj_dim + self.fuse = nn.Sequential( + nn.Linear(fused_dim, out_dim), + nn.SiLU(), + ) + + def forward( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + base = self.base(cond_cont, cond_cat) # (B, cond_out_dim) + s1 = self.stage1_proj(stage1_out).tanh() # (B, stage1_proj_dim) + return self.fuse(torch.cat([base, s1], dim=-1)) # (B, out_dim) + + +class SecondaryDecoder(nn.Module): + """Stage-2 model: predicts vector field over K_MAX secondary slots simultaneously. + + Each slot encodes (stick_break_logit, local_dir_3D, log_mass, charge) for + one secondary ordered by descending energy — mass/charge are the + secondary's predicted physical identity, regressed directly against real + physics targets (see giant.data.transforms.encode_secondaries), used + as-is with no snapping to a discrete PDG code. Padded slots are masked + from loss. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + time_dim: int = 64, + cond_out_dim: int = 128, + stage1_proj_dim: int = 64, + sec_dim: int = SEC_DIM, + dropout: float = 0.1, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.time_emb = SinusoidalEmbedding(time_dim) + self.cond_enc = SecondaryConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + cond_out_dim=cond_out_dim, + stage1_proj_dim=stage1_proj_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + merged_cond_dim = time_dim + cond_out_dim + self.input_proj = nn.Linear(sec_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, sec_dim) + + def forward( + self, + x_t: torch.Tensor, + t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + t_emb = self.time_emb(t) + c_emb = self.cond_enc(cond_cont, cond_cat, stage1_out) + cond = torch.cat([t_emb, c_emb], dim=-1) + x = self.input_proj(x_t) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + +class WGANGenerator(nn.Module): + """Stage-1 WGAN-GP generator: single forward pass, no diffusion/flow time. + + Same `ConditionEncoder` + `ResBlock` trunk as `DenoisingMLP`, but the + input is a noise vector `z` (not a diffused/interpolated `x_t`) and the + ResBlocks condition on the condition encoding alone (no time embedding to + concatenate) — see `giant/model/wgan.py` for the adversarial losses, and + `giant.sample.sample_wgan` for single-pass sampling. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + cond_out_dim: int = 128, + x_dim: int = X_DIM, + noise_dim: int = 64, + dropout: float = 0.1, + k_max: int = K_MAX, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.noise_dim = noise_dim + self.cond_enc = ConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + self.input_proj = nn.Linear(noise_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, cond_out_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, x_dim) + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, k_max + 1), + ) + + def forward( + self, + z: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + cond = self.cond_enc(cond_cont, cond_cat) + x = self.input_proj(z) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + def predict_n_sec( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + """Return n_sec logits (B, K_MAX+1) from conditioning alone.""" + c_emb = self.cond_enc(cond_cont, cond_cat) + return self.n_sec_head(c_emb) + + +class Critic(nn.Module): + """Stage-1 WGAN-GP critic: scalar realism score, own `ConditionEncoder`. + + Kept structurally parallel to `WGANGenerator` (own condition encoder — + separate weights from the generator's, standard GAN practice) but has no + n_sec head: n_sec is never adversarial, it stays a plain classifier on + the generator side. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + cond_out_dim: int = 128, + x_dim: int = X_DIM, + dropout: float = 0.1, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.cond_enc = ConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + self.input_proj = nn.Linear(x_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, cond_out_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_norm = nn.LayerNorm(hidden_dim) + self.out_proj = nn.Linear(hidden_dim, 1) + + def forward( + self, + x: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + cond = self.cond_enc(cond_cont, cond_cat) + h = self.input_proj(x) + for block in self.blocks: + h = block(h, cond) + return self.out_proj(self.out_norm(h)).squeeze(-1) + + +class WGANSecondaryGenerator(nn.Module): + """Stage-2 WGAN-GP generator: single forward pass over all K_MAX slots. + + Mirrors `SecondaryDecoder` minus the time embedding, the same way + `WGANGenerator` mirrors `DenoisingMLP` — takes noise `z` instead of `x_t`, + conditions on `SecondaryConditionEncoder`'s output alone. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + cond_out_dim: int = 128, + stage1_proj_dim: int = 64, + sec_dim: int = SEC_DIM, + noise_dim: int = 64, + dropout: float = 0.1, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.noise_dim = noise_dim + self.cond_enc = SecondaryConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + cond_out_dim=cond_out_dim, + stage1_proj_dim=stage1_proj_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + self.input_proj = nn.Linear(noise_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, cond_out_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, sec_dim) + + def forward( + self, + z: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + cond = self.cond_enc(cond_cont, cond_cat, stage1_out) + x = self.input_proj(z) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + +class SecondaryCritic(nn.Module): + """Stage-2 WGAN-GP critic: scalar realism score over the flattened 90D slots.""" + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + hidden_dim: int = 256, + n_blocks: int = 6, + emb_dim: int = 16, + cond_out_dim: int = 128, + stage1_proj_dim: int = 64, + sec_dim: int = SEC_DIM, + dropout: float = 0.1, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.cond_enc = SecondaryConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + cond_out_dim=cond_out_dim, + stage1_proj_dim=stage1_proj_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + self.input_proj = nn.Linear(sec_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, cond_out_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_norm = nn.LayerNorm(hidden_dim) + self.out_proj = nn.Linear(hidden_dim, 1) + + def forward( + self, + x: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + cond = self.cond_enc(cond_cont, cond_cat, stage1_out) + h = self.input_proj(x) + for block in self.blocks: + h = block(h, cond) + return self.out_proj(self.out_norm(h)).squeeze(-1) + + +class Router(nn.Module): + """Contract for a pluggable mixture-of-experts routing axis.""" + + def __init__(self, n_experts: int) -> None: + super().__init__() + self.n_experts = n_experts + self.gumbel = False + self.gumbel_tau = 1.0 + + def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + raise NotImplementedError + + def combine_weights( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor + ) -> torch.Tensor: + probs = self.gate(cond_cont, cond_cat) + if not (self.gumbel and self.training): + return probs + log_probs = torch.log(probs.clamp_min(1e-8)) + return F.gumbel_softmax(log_probs, tau=self.gumbel_tau, hard=True, dim=-1) + + def top1(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + return self.gate(cond_cont, cond_cat).argmax(dim=-1) + + def balance_loss( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor + ) -> torch.Tensor: + importance = self.gate(cond_cont, cond_cat).sum(dim=0) # (n_experts,) + return (importance.std() / (importance.mean() + 1e-8)) ** 2 + + def classify_loss( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor + ) -> torch.Tensor: + return torch.zeros((), device=cond_cont.device) + + def entropy_loss( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor + ) -> torch.Tensor: + norm_entropy, _ = self.gate_stats(cond_cont, cond_cat) + return norm_entropy + + def gate_stats( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + gate = self.gate(cond_cont, cond_cat) # (B, n_experts) + row_entropy = -(gate * (gate + 1e-8).log()).sum(dim=-1) # (B,) + norm_entropy = row_entropy.mean() / math.log(self.n_experts) + importance = gate.sum(dim=0) # (n_experts,) + return norm_entropy, importance + + +ROUTER_REGISTRY: dict[str, type[Router]] = {} + + +def register_router(name: str): + def decorator(cls: type[Router]) -> type[Router]: + ROUTER_REGISTRY[name] = cls + return cls + + return decorator + + +def build_router(name: str, n_experts: int, **kwargs) -> Router: + if name not in ROUTER_REGISTRY: + raise ValueError( + f"unknown router type {name!r}; available: {sorted(ROUTER_REGISTRY)}" + ) + cls = ROUTER_REGISTRY[name] + accepted = set(inspect.signature(cls.__init__).parameters) - {"self", "n_experts"} + filtered = {k: v for k, v in kwargs.items() if k in accepted} + return cls(n_experts=n_experts, **filtered) + + +def _bounded_interp(raw: torch.Tensor, lo: float, hi: float) -> torch.Tensor: + return lo + (hi - lo) * torch.sigmoid(raw) + + +def _inverse_bounded_interp(value: float, lo: float, hi: float) -> float: + p = min(max((value - lo) / (hi - lo), 1e-6), 1 - 1e-6) + return math.log(p / (1 - p)) + + +@register_router("energy") +class EnergyRouter(Router): + def __init__( + self, + n_experts: int = 4, + temperature: float = 0.5, + learn_centers: bool = True, + energy_idx: int = 3, + centers_init: Sequence[float] | None = None, + learn_width: bool = False, + learn_temperature: bool = False, + width_min_ratio: float = 0.1, + width_max_ratio: float = 10.0, + ) -> None: + super().__init__(n_experts) + if learn_width and learn_temperature: + raise ValueError("learn_width and learn_temperature are mutually exclusive") + self.temperature = temperature + self.energy_idx = energy_idx + self.learn_width = learn_width + self.learn_temperature = learn_temperature + if learn_width or learn_temperature: + if not (width_min_ratio < 1.0 < width_max_ratio): + raise ValueError( + f"width_min_ratio ({width_min_ratio}) and width_max_ratio " + f"({width_max_ratio}) must bracket 1.0" + ) + self._width_lo = width_min_ratio * temperature + self._width_hi = width_max_ratio * temperature + raw0 = _inverse_bounded_interp(temperature, self._width_lo, self._width_hi) + if learn_width: + self.raw_width = nn.Parameter(torch.full((n_experts,), raw0)) + else: + self.raw_temperature = nn.Parameter(torch.tensor(raw0)) + if centers_init is None: + centers = torch.linspace(-2.0, 2.0, n_experts) + else: + if len(centers_init) != n_experts: + raise ValueError( + f"centers_init has {len(centers_init)} values, " + f"expected n_experts={n_experts}" + ) + centers = torch.tensor(list(centers_init), dtype=torch.float32) + if learn_centers: + self.centers = nn.Parameter(centers) + else: + self.register_buffer("centers", centers) + + def effective_width(self) -> torch.Tensor | float: + if self.learn_width: + return _bounded_interp(self.raw_width, self._width_lo, self._width_hi) + if self.learn_temperature: + return _bounded_interp(self.raw_temperature, self._width_lo, self._width_hi) + return self.temperature + + def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + e = cond_cont[:, self.energy_idx].unsqueeze(-1) # (B, 1) + d2 = (e - self.centers.unsqueeze(0)) ** 2 # (B, n_experts) + return torch.softmax(-d2 / self.effective_width(), dim=-1) + + +@register_router("pdg") +class PdgRouter(Router): + def __init__( + self, + n_experts: int, + pdg_vocab: int, + emb_dim: int = 8, + temperature: float = 0.5, + learn_centers: bool = True, + ) -> None: + super().__init__(n_experts) + self.temperature = temperature + self.pdg_emb = nn.Embedding(pdg_vocab, emb_dim) + centers = torch.randn(n_experts, emb_dim) * 0.1 + if learn_centers: + self.centers = nn.Parameter(centers) + else: + self.register_buffer("centers", centers) + + def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + e = self.pdg_emb(cond_cat[:, 0]) # (B, emb_dim) + d2 = ((e.unsqueeze(1) - self.centers.unsqueeze(0)) ** 2).sum( + -1 + ) # (B, n_experts) + return torch.softmax(-d2 / self.temperature, dim=-1) + + +@register_router("process") +class ProcessRouter(Router): + def __init__( + self, + n_experts: int, + pdg_vocab: int, + mat_vocab: int, + emb_dim: int = 8, + hidden_dim: int = 64, + ) -> None: + super().__init__(n_experts) + self.pdg_emb = nn.Embedding(pdg_vocab, emb_dim) + self.mat_emb = nn.Embedding(mat_vocab, emb_dim) + self.classifier = nn.Sequential( + nn.Linear(COND_DIM + 2 * emb_dim, hidden_dim), + nn.SiLU(), + nn.Linear(hidden_dim, n_experts), + ) + + def logits(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + pdg_e = self.pdg_emb(cond_cat[:, 0]) + mat_e = self.mat_emb(cond_cat[:, 1]) + h = torch.cat([cond_cont, pdg_e, mat_e], dim=-1) + return self.classifier(h) + + def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + return torch.softmax(self.logits(cond_cont, cond_cat), dim=-1) + + def classify_loss( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor + ) -> torch.Tensor: + return F.cross_entropy(self.logits(cond_cont, cond_cat), labels) + + +class ComposedRouter(Router): + def __init__(self, routers: list[Router]) -> None: + if not routers: + raise ValueError("ComposedRouter needs at least one sub-router") + n_experts = 1 + for r in routers: + n_experts *= r.n_experts + super().__init__(n_experts) + self.routers = nn.ModuleList(routers) + + def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: + joint = self.routers[0].gate(cond_cont, cond_cat) # (B, n_0) + for router in self.routers[1:]: + g = router.gate(cond_cont, cond_cat) # (B, n_i) + joint = (joint.unsqueeze(-1) * g.unsqueeze(1)).flatten( + 1 + ) # (B, prod so far) + return joint + + def classify_loss( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor + ) -> torch.Tensor: + total = torch.zeros((), device=cond_cont.device) + for router in self.routers: + total = total + router.classify_loss(cond_cont, cond_cat, labels) + return total + + +def build_composed_router(specs: list[dict], **shared_kwargs) -> ComposedRouter: + routers = [ + build_router( + spec["type"], + spec["n_experts"], + **{ + **shared_kwargs, + **{k: v for k, v in spec.items() if k not in ("type", "n_experts")}, + }, + ) + for spec in specs + ] + return ComposedRouter(routers) + + +class ExpertTrunk(nn.Module): + def __init__( + self, + in_dim: int, + hidden_dim: int, + n_blocks: int, + merged_cond_dim: int, + dropout: float = 0.1, + ) -> None: + super().__init__() + self.input_proj = nn.Linear(in_dim, hidden_dim) + self.blocks = nn.ModuleList( + [ + ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) + for _ in range(n_blocks) + ] + ) + self.out_proj = nn.Linear(hidden_dim, in_dim) + + def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + x = self.input_proj(x) + for block in self.blocks: + x = block(x, cond) + return self.out_proj(x) + + +def _route_forward( + experts: nn.ModuleList, + router: Router, + x: torch.Tensor, + cond: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + training: bool, +) -> torch.Tensor: + if training: + weights = router.combine_weights(cond_cont, cond_cat) # (B, n_experts) + out = torch.zeros_like(x) + for i, expert in enumerate(experts): + out = out + weights[:, i : i + 1] * expert(x, cond) + return out + + idx = router.top1(cond_cont, cond_cat) # (B,) + out = torch.zeros_like(x) + for i, expert in enumerate(experts): + mask = idx == i + if mask.any(): + out[mask] = expert(x[mask], cond[mask]) + return out + + +class RoutedDenoisingMLP(nn.Module): + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + router: Router, + expert_hidden_dim: int = 128, + expert_n_blocks: int = 3, + emb_dim: int = EMB_DIM, + time_dim: int = 64, + cond_out_dim: int = 128, + x_dim: int = X_DIM, + dropout: float = 0.1, + k_max: int = K_MAX, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.router = router + self.time_emb = SinusoidalEmbedding(time_dim) + self.cond_enc = ConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + merged_cond_dim = time_dim + cond_out_dim + self.experts = nn.ModuleList( + [ + ExpertTrunk( + x_dim, + expert_hidden_dim, + expert_n_blocks, + merged_cond_dim, + dropout=dropout, + ) + for _ in range(router.n_experts) + ] + ) + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, cond_out_dim), + nn.SiLU(), + nn.Linear(cond_out_dim, k_max + 1), + ) + + def forward( + self, + x_t: torch.Tensor, + t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + t_emb = self.time_emb(t) + c_emb = self.cond_enc(cond_cont, cond_cat) + cond = torch.cat([t_emb, c_emb], dim=-1) + return _route_forward( + self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training + ) + + def predict_n_sec( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + ) -> torch.Tensor: + c_emb = self.cond_enc(cond_cont, cond_cat) + return self.n_sec_head(c_emb) + + +class RoutedSecondaryDecoder(nn.Module): + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + router: Router, + expert_hidden_dim: int = 128, + expert_n_blocks: int = 3, + emb_dim: int = EMB_DIM, + time_dim: int = 64, + cond_out_dim: int = 128, + stage1_proj_dim: int = 64, + sec_dim: int = SEC_DIM, + dropout: float = 0.1, + conditioning: str = "embedding", + ) -> None: + super().__init__() + self.router = router + self.time_emb = SinusoidalEmbedding(time_dim) + self.cond_enc = SecondaryConditionEncoder( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + emb_dim=emb_dim, + cond_out_dim=cond_out_dim, + stage1_proj_dim=stage1_proj_dim, + out_dim=cond_out_dim, + conditioning=conditioning, + ) + merged_cond_dim = time_dim + cond_out_dim + self.experts = nn.ModuleList( + [ + ExpertTrunk( + sec_dim, + expert_hidden_dim, + expert_n_blocks, + merged_cond_dim, + dropout=dropout, + ) + for _ in range(router.n_experts) + ] + ) + + def forward( + self, + x_t: torch.Tensor, + t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + t_emb = self.time_emb(t) + c_emb = self.cond_enc(cond_cont, cond_cat, stage1_out) + cond = torch.cat([t_emb, c_emb], dim=-1) + return _route_forward( + self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training + ) + + +_STAGE1_MODEL_KEYS = { + "pdg_vocab", + "mat_vocab", + "hidden_dim", + "n_blocks", + "emb_dim", + "dropout", + "k_max", + "conditioning", +} +_SEC_DECODER_MODEL_KEYS = { + "pdg_vocab", + "mat_vocab", + "hidden_dim", + "n_blocks", + "emb_dim", + "dropout", + "conditioning", +} +_WGAN_GENERATOR_MODEL_KEYS = _STAGE1_MODEL_KEYS | {"noise_dim"} +_WGAN_SEC_GENERATOR_MODEL_KEYS = _SEC_DECODER_MODEL_KEYS | {"noise_dim"} +_CRITIC_MODEL_KEYS = _STAGE1_MODEL_KEYS - {"k_max"} + + +_AXIS_KEY_RE = re.compile(r"^axis(\d+)_(.+)$") + + +def _parse_composed_axes(router_cfg: dict) -> list[dict]: + axes: dict[int, dict] = {} + for key, value in router_cfg.items(): + m = _AXIS_KEY_RE.match(key) + if m is None: + continue + idx, field = int(m.group(1)), m.group(2) + axes.setdefault(idx, {})[field] = value + missing = set(range(len(axes))) - axes.keys() + if missing: + raise ValueError(f"composed router config has gaps at axis indices {missing}") + return [axes[i] for i in range(len(axes))] + + +_VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process") + + +def _check_router_conditioning_compat( + router_types: list[str], conditioning: str +) -> None: + bad = sorted(set(router_types) & set(_VOCAB_SCOPED_ROUTER_TYPES)) + if bad and conditioning == "physical": + raise ValueError( + f"router type(s) {bad} always use a training-vocab PDG embedding, " + "which is incompatible with conditioning='physical' (whose whole " + "point is generalizing beyond that vocab) — pick a different " + "router type (e.g. 'energy') or use conditioning='embedding'." + ) + + +def _build_router_from_cfg( + router_cfg: dict, pdg_vocab: int, mat_vocab: int, conditioning: str = "embedding" +) -> Router: + shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab) + if router_cfg["type"] == "composed": + axes = _parse_composed_axes(router_cfg) + _check_router_conditioning_compat([a["type"] for a in axes], conditioning) + router = build_composed_router(axes, **shared_vocab) + router.gumbel = bool(router_cfg.get("gumbel", False)) + return router + _check_router_conditioning_compat([router_cfg["type"]], conditioning) + router_kwargs = { + k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts") + } + router_kwargs.setdefault("pdg_vocab", pdg_vocab) + router_kwargs.setdefault("mat_vocab", mat_vocab) + router = build_router(router_cfg["type"], router_cfg["n_experts"], **router_kwargs) + router.gumbel = bool(router_cfg.get("gumbel", False)) + return router + + +def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]: + if model_config.get("mode") == "wgan": + stage1 = WGANGenerator( + **{k: v for k, v in model_config.items() if k in _WGAN_GENERATOR_MODEL_KEYS} + ) + sec_decoder = WGANSecondaryGenerator( + **{ + k: v + for k, v in model_config.items() + if k in _WGAN_SEC_GENERATOR_MODEL_KEYS + } + ) + return stage1, sec_decoder + + router_cfg = model_config.get("router") + if router_cfg and router_cfg.get("enabled"): + pdg_vocab = model_config["pdg_vocab"] + mat_vocab = model_config["mat_vocab"] + shared = dict( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + expert_hidden_dim=model_config.get("expert_hidden_dim") + or model_config.get("hidden_dim", 128), + expert_n_blocks=model_config.get("expert_n_blocks") + or model_config.get("n_blocks", 3), + emb_dim=model_config.get("emb_dim", EMB_DIM), + dropout=model_config.get("dropout", 0.1), + conditioning=model_config.get("conditioning", "embedding"), + ) + conditioning = shared["conditioning"] + stage1 = RoutedDenoisingMLP( + router=_build_router_from_cfg( + router_cfg, pdg_vocab, mat_vocab, conditioning + ), + k_max=model_config.get("k_max", K_MAX), + **shared, + ) + sec_decoder = RoutedSecondaryDecoder( + router=_build_router_from_cfg( + router_cfg, pdg_vocab, mat_vocab, conditioning + ), + **shared, + ) + return stage1, sec_decoder + + stage1 = DenoisingMLP( + **{k: v for k, v in model_config.items() if k in _STAGE1_MODEL_KEYS} + ) + sec_decoder = SecondaryDecoder( + **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} + ) + return stage1, sec_decoder + + +def build_critics(model_config: dict) -> tuple[nn.Module, nn.Module]: + critic = Critic( + **{k: v for k, v in model_config.items() if k in _CRITIC_MODEL_KEYS} + ) + sec_critic = SecondaryCritic( + **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} + ) + return critic, sec_critic diff --git a/tests/test_flow.py b/tests/test_flow.py index b9e7e8c..6a6730e 100644 --- a/tests/test_flow.py +++ b/tests/test_flow.py @@ -1,12 +1,31 @@ +import pytest import torch from giant.constants import COND_DIM -from giant.model.network import DenoisingMLP +from giant.model.network import Stage1Model from giant.model.schedule import CosineSchedule, flow_matching_loss from giant.sample import sample_flow, sample_ddim +PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + +_SAMPLE_XFAIL_REASON = ( + "giant/sample.py isn't updated yet — its sample_flow/sample_ddim call " + "models positionally as model(x, t, cond_cont, cond_cat), which doesn't " + "match Stage1Model's new forward signature. Deferred to " + "docs/v0.3.0-design.md step 6." +) + def _small_model(): - return DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2) + return Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=32, + n_res_blocks=2, + n_sec_head_k_max=15, + ) def _batch(B=8): @@ -35,6 +54,7 @@ def test_flow_matching_loss_has_grad(): assert any(p.grad is not None for p in model.parameters()) +@pytest.mark.xfail(reason=_SAMPLE_XFAIL_REASON, strict=False) def test_sample_flow_shape(): B = 6 cond_cont = torch.randn(B, COND_DIM) @@ -51,6 +71,7 @@ def test_ddpm_loss_nonneg(): assert loss.item() >= 0.0 +@pytest.mark.xfail(reason=_SAMPLE_XFAIL_REASON, strict=False) def test_sample_ddim_shape(): B = 4 schedule = CosineSchedule(T=50) diff --git a/tests/test_migration_v02_v03.py b/tests/test_migration_v02_v03.py new file mode 100644 index 0000000..488bf37 --- /dev/null +++ b/tests/test_migration_v02_v03.py @@ -0,0 +1,241 @@ +"""Migration acceptance test for v0.3.0 step 2 (docs/v0.3.0-design.md §4.3, +§12 step 2): "load a v0.2 checkpoint through migrate_config + the new +build_models, and diff its outputs against v0.2 code on the same input +batch — bit-identical, or the refactor has changed something it should not +have." + +No `/ceph` access on this machine (see CLAUDE.md's Compute environment +section), so a real trained checkpoint can't be used here — see +docs/v0.3.0-design.md's plan for the separate portal-machine follow-up with a +real checkpoint. This test is the synthetic stand-in: build a v0.2-shaped +model from the frozen `tests/legacy/network_v02_snapshot.py` classes with +fixed-seed random weights (playing the role of "a v0.2 checkpoint"), migrate +its config and remap its state dict onto the new `build_models` output, and +assert the two produce bit-identical output on the same random input batch. +""" + +import torch + +from giant.constants import COND_DIM, SEC_SLOT_DIM, X_DIM +from giant.model import network as net +from tests.legacy import network_v02_snapshot as legacy + +PDG_VOCAB = 12 +MAT_VOCAB = 4 +HIDDEN_DIM = 32 +N_BLOCKS = 2 +EMB_DIM = 8 +K = 6 # small k_max for a fast test +BATCH = 5 + + +def _legacy_model_config(mode: str, conditioning: str) -> dict: + return { + "pdg_vocab": PDG_VOCAB, + "mat_vocab": MAT_VOCAB, + "hidden_dim": HIDDEN_DIM, + "n_blocks": N_BLOCKS, + "emb_dim": EMB_DIM, + "dropout": 0.0, + "k_max": K, + "conditioning": conditioning, + "router": {"enabled": False}, + "mode": mode, + "noise_dim": 16, + } + + +def _random_batch(seed: int): + g = torch.Generator().manual_seed(seed) + cond_cont = torch.randn(BATCH, COND_DIM, generator=g) + cond_cat = torch.randint(0, min(PDG_VOCAB, MAT_VOCAB), (BATCH, 2), generator=g) + x1 = torch.randn(BATCH, X_DIM, generator=g) + x2 = torch.randn(BATCH, K * SEC_SLOT_DIM, generator=g) + t = torch.rand(BATCH, generator=g) + return cond_cont, cond_cat, x1, x2, t + + +def _assert_bit_identical(a: torch.Tensor, b: torch.Tensor, label: str) -> None: + assert a.shape == b.shape, f"{label}: shape mismatch {a.shape} vs {b.shape}" + assert torch.equal(a, b), ( + f"{label}: outputs diverged, max abs diff = {(a - b).abs().max().item()}" + ) + + +def _run_migration_check(mode: str, conditioning: str) -> None: + torch.manual_seed(0) + legacy_cfg = _legacy_model_config(mode, conditioning) + + if mode == "wgan": + old_stage1 = legacy.WGANGenerator( + pdg_vocab=PDG_VOCAB, + mat_vocab=MAT_VOCAB, + hidden_dim=HIDDEN_DIM, + n_blocks=N_BLOCKS, + emb_dim=EMB_DIM, + noise_dim=16, + dropout=0.0, + k_max=K, + conditioning=conditioning, + ) + old_stage2 = legacy.WGANSecondaryGenerator( + pdg_vocab=PDG_VOCAB, + mat_vocab=MAT_VOCAB, + hidden_dim=HIDDEN_DIM, + n_blocks=N_BLOCKS, + emb_dim=EMB_DIM, + sec_dim=K * SEC_SLOT_DIM, + noise_dim=16, + dropout=0.0, + conditioning=conditioning, + ) + else: + old_stage1 = legacy.DenoisingMLP( + pdg_vocab=PDG_VOCAB, + mat_vocab=MAT_VOCAB, + hidden_dim=HIDDEN_DIM, + n_blocks=N_BLOCKS, + emb_dim=EMB_DIM, + dropout=0.0, + k_max=K, + conditioning=conditioning, + ) + old_stage2 = legacy.SecondaryDecoder( + pdg_vocab=PDG_VOCAB, + mat_vocab=MAT_VOCAB, + hidden_dim=HIDDEN_DIM, + n_blocks=N_BLOCKS, + emb_dim=EMB_DIM, + sec_dim=K * SEC_SLOT_DIM, + dropout=0.0, + conditioning=conditioning, + ) + old_stage1.eval() + old_stage2.eval() + + cond_cont, cond_cat, x1, x2, t = _random_batch(seed=123) + z1 = torch.randn(BATCH, 16, generator=torch.Generator().manual_seed(456)) + z2 = torch.randn(BATCH, 16, generator=torch.Generator().manual_seed(789)) + + with torch.no_grad(): + if mode == "wgan": + old_out1 = old_stage1(z1, cond_cont, cond_cat) + else: + old_out1 = old_stage1(x1, t, cond_cont, cond_cat) + old_n_sec = old_stage1.predict_n_sec(cond_cont, cond_cat) + if mode == "wgan": + old_out2 = old_stage2(z2, cond_cont, cond_cat, old_out1) + else: + old_out2 = old_stage2(x2, t, cond_cont, cond_cat, old_out1) + + # --- migrate: config + state dict, through the new build_models --- + new_models = net.build_models(legacy_cfg) + new_stage1, new_stage2 = new_models["stage1"], new_models["stage2"] + assert isinstance(new_stage1, net.Stage1Model) + assert isinstance(new_stage2, net.Stage2OneShot) + # legacy_owner="stage1": n_sec lives on stage1, not stage2, for a + # migrated v0.2 checkpoint (design doc §4.1). + assert new_stage1.n_sec_head is not None + assert new_stage2.n_sec_head is None + + remapped1, remapped2 = net.migrate_legacy_state_dict( + old_stage1.state_dict(), old_stage2.state_dict() + ) + missing1, unexpected1 = new_stage1.load_state_dict(remapped1, strict=True) + missing2, unexpected2 = new_stage2.load_state_dict(remapped2, strict=True) + assert not missing1 and not unexpected1 + assert not missing2 and not unexpected2 + new_stage1.eval() + new_stage2.eval() + + with torch.no_grad(): + if mode == "wgan": + new_out1 = new_stage1(z1, cond_cont, cond_cat) + else: + new_out1 = new_stage1(x1, cond_cont, cond_cat, t=t) + new_n_sec = new_stage1.predict_n_sec(cond_cont, cond_cat) + if mode == "wgan": + new_out2 = new_stage2(z2, cond_cont, cond_cat, new_out1) + else: + new_out2 = new_stage2(x2, cond_cont, cond_cat, new_out1, t=t) + + _assert_bit_identical(old_out1, new_out1, f"stage1 output ({mode}, {conditioning})") + _assert_bit_identical( + old_n_sec, new_n_sec, f"n_sec logits ({mode}, {conditioning})" + ) + _assert_bit_identical(old_out2, new_out2, f"stage2 output ({mode}, {conditioning})") + + +def test_migration_flow_embedding(): + _run_migration_check(mode="flow", conditioning="embedding") + + +def test_migration_flow_physical(): + _run_migration_check(mode="flow", conditioning="physical") + + +def test_migration_wgan_embedding(): + _run_migration_check(mode="wgan", conditioning="embedding") + + +def test_migration_wgan_physical(): + _run_migration_check(mode="wgan", conditioning="physical") + + +def test_migrate_legacy_model_config_shape(): + """_migrate_legacy_model_config produces the nested shape build_models + expects, with the legacy_owner marker set so build_models routes the + n_sec head back onto stage 1.""" + legacy_cfg = _legacy_model_config(mode="flow", conditioning="physical") + migrated = net._migrate_legacy_model_config(legacy_cfg) + assert migrated["pdg_vocab"] == PDG_VOCAB + assert migrated["mat_vocab"] == MAT_VOCAB + assert migrated["conditioning"]["particle"]["type"] == "physical" + assert migrated["conditioning"]["particle"]["n_layers"] == 2 + assert migrated["conditioning"]["material"]["n_layers"] == 2 + assert migrated["stage1_model"]["hidden_dim"] == HIDDEN_DIM + assert migrated["stage2_model"]["n_sec"]["legacy_owner"] == "stage1" + assert migrated["stage2_model"]["decoder"] == "one_shot" + + +def test_build_models_accepts_new_nested_shape_unchanged(): + """A dict that already has a 'stage1_model' key (the new shape) is + passed through build_models without going through the legacy migration + path at all.""" + cfg = { + "pdg_vocab": PDG_VOCAB, + "mat_vocab": MAT_VOCAB, + "conditioning": { + "out_dim": 32, + "particle": {"type": "physical", "emb_dim": EMB_DIM, "n_layers": 1}, + "material": {"type": "physical", "emb_dim": EMB_DIM, "n_layers": 1}, + }, + "stage1_model": { + "active": True, + "generator": "flow", + "hidden_dim": HIDDEN_DIM, + "n_res_blocks": N_BLOCKS, + "dropout": 0.0, + "flow": {"time_dim": 16}, + "router": {"enabled": False}, + }, + "stage2_model": { + "active": True, + "decoder": "one_shot", + "generator": "flow", + "hidden_dim": HIDDEN_DIM, + "n_res_blocks": N_BLOCKS, + "dropout": 0.0, + "k_max": K, + "context_dim": 16, + "n_sec": {"mode": "head"}, + "flow": {"time_dim": 16}, + "router": {"enabled": False, "tie_to_stage1": False}, + }, + } + models = net.build_models(cfg) + assert isinstance(models["stage1"], net.Stage1Model) + assert isinstance(models["stage2"], net.Stage2OneShot) + # Fresh v0.3.0 config, no legacy_owner: n_sec lives on stage 2. + assert models["stage1"].n_sec_head is None + assert models["stage2"].n_sec_head is not None diff --git a/tests/test_network.py b/tests/test_network.py index 397aa79..0bb99b5 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -1,6 +1,9 @@ import torch from giant.constants import COND_DIM -from giant.model.network import DenoisingMLP, SinusoidalEmbedding +from giant.model.network import SinusoidalEmbedding, Stage1Model + +PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} def test_sinusoidal_embedding_shape(): @@ -15,9 +18,15 @@ def test_sinusoidal_embedding_batch_1(): assert emb(t).shape == (1, 32) -def test_denoising_mlp_output_shape(): +def test_stage1_model_output_shape(): B = 8 - model = DenoisingMLP(pdg_vocab=5, mat_vocab=3) + model = Stage1Model( + pdg_vocab=5, + mat_vocab=3, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + n_sec_head_k_max=15, + ) x_t = torch.randn(B, 9) t = torch.rand(B) cond_cont = torch.randn(B, COND_DIM) @@ -28,20 +37,37 @@ def test_denoising_mlp_output_shape(): ], dim=1, ) - out = model(x_t, t, cond_cont, cond_cat) + out = model(x_t, cond_cont, cond_cat, t=t) assert out.shape == (B, 9) -def test_denoising_mlp_gradients_flow(): +def test_stage1_model_gradients_flow(): B = 4 - model = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2) + model = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=32, + n_res_blocks=2, + n_sec_head_k_max=15, + ) x_t = torch.randn(B, 9) t = torch.rand(B) cond_cont = torch.randn(B, COND_DIM) cond_cat = torch.zeros(B, 2, dtype=torch.long) # Both paths must be exercised to get gradients through all parameters. - flow_loss = model(x_t, t, cond_cont, cond_cat).sum() + flow_loss = model(x_t, cond_cont, cond_cat, t=t).sum() nsec_loss = model.predict_n_sec(cond_cont, cond_cat).sum() (flow_loss + nsec_loss).backward() for name, p in model.named_parameters(): assert p.grad is not None, f"no grad for {name}" + + +def test_stage1_model_no_n_sec_head_by_default(): + """Fresh v0.3.0 construction (no n_sec_head_k_max) has no n_sec head — + decision 1 (docs/v0.3.0-design.md §2) moves it to stage 2.""" + model = Stage1Model( + pdg_vocab=3, mat_vocab=2, particle_cfg=PARTICLE_CFG, material_cfg=MATERIAL_CFG + ) + assert model.n_sec_head is None diff --git a/tests/test_phase2.py b/tests/test_phase2.py index c8e7fd1..deeb9c7 100644 --- a/tests/test_phase2.py +++ b/tests/test_phase2.py @@ -5,31 +5,50 @@ import pytest import torch from giant.constants import COND_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_DIM, X_DIM -from giant.model.network import DenoisingMLP, SecondaryDecoder +from giant.model.network import Stage1Model, Stage2OneShot from giant.model.schedule import flow_matching_loss_secondary from giant.sample import sample_secondaries +_SAMPLE_SECONDARIES_XFAIL_REASON = ( + "giant/sample.py isn't updated yet — sample_secondaries calls the " + "decoder positionally as decoder(x, t, cond_cont, cond_cat, stage1_out), " + "which doesn't match Stage2OneShot's new forward signature. Deferred to " + "docs/v0.3.0-design.md step 6." +) + # ── helpers ────────────────────────────────────────────────────────────────── +def _particle_material_cfg(conditioning: str) -> tuple[dict, dict]: + cfg = {"type": conditioning, "emb_dim": 16, "n_layers": 1} + return dict(cfg), dict(cfg) + + def _stage1(pdg=3, mat=2, conditioning="embedding"): - return DenoisingMLP( + particle_cfg, material_cfg = _particle_material_cfg(conditioning) + return Stage1Model( pdg_vocab=pdg, mat_vocab=mat, + particle_cfg=particle_cfg, + material_cfg=material_cfg, hidden_dim=32, - n_blocks=2, - conditioning=conditioning, + n_res_blocks=2, + n_sec_head_k_max=K_MAX, ) def _sec_decoder(pdg=3, mat=2, conditioning="embedding"): - return SecondaryDecoder( + particle_cfg, material_cfg = _particle_material_cfg(conditioning) + return Stage2OneShot( pdg_vocab=pdg, mat_vocab=mat, + particle_cfg=particle_cfg, + material_cfg=material_cfg, hidden_dim=32, - n_blocks=2, - conditioning=conditioning, + n_res_blocks=2, + generator="flow", + time_dim=16, ) @@ -41,7 +60,7 @@ def _cond(B=8, pdg=3, mat=2): return cond_cont, cond_cat -# ── DenoisingMLP Phase-2 additions ─────────────────────────────────────────── +# ── Stage1Model Phase-2 additions ──────────────────────────────────────────── def test_predict_n_sec_shape(): @@ -82,7 +101,7 @@ def test_condition_encoder_embedding_mode_has_embedding_tables(): assert not hasattr(model.cond_enc, "particle_mlp") -# ── SecondaryDecoder ────────────────────────────────────────────────────────── +# ── Stage2OneShot ───────────────────────────────────────────────────────────── @pytest.mark.parametrize("conditioning", ["embedding", "physical"]) @@ -93,7 +112,7 @@ def test_sec_decoder_output_shape(conditioning): t = torch.rand(B) cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) - out = decoder(x_t, t, cond_cont, cond_cat, stage1_out) + out = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t) assert out.shape == (B, SEC_DIM) @@ -104,7 +123,7 @@ def test_sec_decoder_no_nan(): t = torch.rand(B) cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) - out = decoder(x_t, t, cond_cont, cond_cat, stage1_out) + out = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t) assert torch.isfinite(out).all() @@ -115,7 +134,9 @@ def test_sec_decoder_gradients(): t = torch.rand(B) cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) - decoder(x_t, t, cond_cont, cond_cat, stage1_out).sum().backward() + flow_out = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t).sum() + nsec_out = decoder.predict_n_sec(cond_cont, cond_cat, stage1_out).sum() + (flow_out + nsec_out).backward() for name, p in decoder.named_parameters(): assert p.grad is not None, f"no grad for {name}" @@ -167,6 +188,7 @@ def test_flow_matching_loss_secondary_has_grad(): # ── sampling ────────────────────────────────────────────────────────────────── +@pytest.mark.xfail(reason=_SAMPLE_SECONDARIES_XFAIL_REASON, strict=False) def test_sample_secondaries_shapes(): B, pdg, mat = 6, 3, 2 decoder = _sec_decoder(pdg, mat) @@ -182,6 +204,7 @@ def test_sample_secondaries_shapes(): assert sec_valid.dtype == torch.bool +@pytest.mark.xfail(reason=_SAMPLE_SECONDARIES_XFAIL_REASON, strict=False) def test_sample_secondaries_valid_mask_matches_n_sec(): B, pdg, mat = 4, 3, 2 decoder = _sec_decoder(pdg, mat) diff --git a/tests/test_rollout.py b/tests/test_rollout.py index 5227109..bdc0006 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -8,24 +8,46 @@ import numpy as np import pytest import torch -from giant.constants import TERM_ESCAPED, TERM_MAX_STEPS, TERM_UNKNOWN_PDG +from giant.constants import TERM_ESCAPED, TERM_MAX_STEPS, TERM_UNKNOWN_PDG, K_MAX from giant.data.transforms import Normalizer -from giant.model.network import DenoisingMLP, SecondaryDecoder +from giant.model.network import Stage1Model, Stage2OneShot from giant.rollout import make_seed_frontier, rollout pytest.importorskip("sklearn") from giant import geometry as g # noqa: E402 +# giant/rollout.py isn't updated yet — it drives Stage1Model/Stage2OneShot +# through giant.sample's sample_flow/sample_secondaries, which still call +# models with the pre-refactor positional convention +# (model(x, t, cond_cont, cond_cat)) that no longer matches these classes' +# forward signatures. Deferred to docs/v0.3.0-design.md step 6/§10. +pytestmark = pytest.mark.xfail(reason="giant/rollout.py not updated for v0.3.0 network.py yet (step 6)", strict=False) + PDG_MAP = {22: 0, 11: 1, -11: 2} MAT_MAP = {"G4_AIR": 0, "G4_PbWO4": 1} def _models(conditioning="embedding"): - s1 = DenoisingMLP( - pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2, conditioning=conditioning + particle_cfg = {"type": conditioning, "emb_dim": 16, "n_layers": 1} + material_cfg = {"type": conditioning, "emb_dim": 16, "n_layers": 1} + s1 = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + n_sec_head_k_max=K_MAX, ) - s2 = SecondaryDecoder( - pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2, conditioning=conditioning + s2 = Stage2OneShot( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator="flow", + time_dim=16, ) return s1.eval(), s2.eval() diff --git a/tests/test_router.py b/tests/test_router.py index a8e9174..4e8b044 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -6,19 +6,22 @@ import torch from giant.constants import COND_DIM, K_MAX, SEC_DIM, X_DIM from giant.model.network import ( ComposedRouter, - DenoisingMLP, EnergyRouter, + MonolithicTrunk, PdgRouter, ProcessRouter, ROUTER_REGISTRY, - RoutedDenoisingMLP, - RoutedSecondaryDecoder, - SecondaryDecoder, + RoutedTrunk, + Stage1Model, + Stage2OneShot, build_composed_router, build_models, build_router, ) +PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + def _cond(B=8, pdg=3, mat=2): cond_cont = torch.randn(B, COND_DIM) @@ -30,23 +33,30 @@ def _cond(B=8, pdg=3, mat=2): def _routed_stage1(n_experts=4, pdg=3, mat=2, **router_kwargs): router = build_router("energy", n_experts, **router_kwargs) - return RoutedDenoisingMLP( + return Stage1Model( pdg_vocab=pdg, mat_vocab=mat, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=2, router=router, - expert_hidden_dim=16, - expert_n_blocks=2, + n_sec_head_k_max=K_MAX, ) def _routed_sec_decoder(n_experts=4, pdg=3, mat=2, **router_kwargs): router = build_router("energy", n_experts, **router_kwargs) - return RoutedSecondaryDecoder( + return Stage2OneShot( pdg_vocab=pdg, mat_vocab=mat, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=2, + generator="flow", + time_dim=16, router=router, - expert_hidden_dim=16, - expert_n_blocks=2, ) @@ -92,7 +102,7 @@ def test_energy_router_balance_loss_is_nonnegative_scalar(): def test_build_router_ignores_unrecognized_kwargs(): - # lambda_balance is a model_config.router key but not an EnergyRouter kwarg + # lambda_balance is a router config key but not an EnergyRouter kwarg router = build_router("energy", 4, temperature=0.3, lambda_balance=0.5) assert isinstance(router, EnergyRouter) assert router.temperature == 0.3 @@ -375,18 +385,18 @@ def test_build_router_from_cfg_sets_gumbel_for_composed_router(): assert router.gumbel is True -def test_routed_denoising_mlp_forward_runs_with_gumbel_enabled(): +def test_routed_stage1_forward_runs_with_gumbel_enabled(): """End-to-end forward through _route_forward's train branch with straight-through Gumbel-softmax combine weights enabled.""" B = 8 model = _routed_stage1(n_experts=3) - model.router.gumbel = True - model.router.gumbel_tau = 0.5 + model.trunk.router.gumbel = True + model.trunk.router.gumbel_tau = 0.5 model.train() x_t = torch.randn(B, X_DIM) t = torch.rand(B) cond_cont, cond_cat = _cond(B) - out = model(x_t, t, cond_cont, cond_cat) + out = model(x_t, cond_cont, cond_cat, t=t) assert out.shape == (B, X_DIM) assert torch.isfinite(out).all() @@ -463,59 +473,89 @@ def test_build_router_pdg_type_uses_pdg_vocab(): assert router.pdg_emb.num_embeddings == 5 +def _nested_cfg( + pdg_vocab, + mat_vocab, + stage1_router=None, + stage2_router=None, + particle_type="physical", + material_type="physical", + **overrides, +): + """Minimal new-shape (v0.3.0) model_config for build_models, with + optional router sub-blocks. `overrides` deep-patches stage1_model.""" + stage1_model = { + "active": True, + "generator": "flow", + "hidden_dim": 16, + "n_res_blocks": 2, + "dropout": 0.0, + "flow": {"time_dim": 16}, + "router": stage1_router or {"enabled": False}, + } + stage1_model.update(overrides) + return { + "pdg_vocab": pdg_vocab, + "mat_vocab": mat_vocab, + "conditioning": { + "out_dim": 32, + "particle": {"type": particle_type, "emb_dim": 8, "n_layers": 1}, + "material": {"type": material_type, "emb_dim": 8, "n_layers": 1}, + }, + "stage1_model": stage1_model, + "stage2_model": { + "active": True, + "decoder": "one_shot", + "generator": "flow", + "hidden_dim": 16, + "n_res_blocks": 2, + "dropout": 0.0, + "k_max": K_MAX, + "context_dim": 16, + "n_sec": {"mode": "head"}, + "flow": {"time_dim": 16}, + "router": stage2_router or {"enabled": False, "tie_to_stage1": False}, + }, + } + + def test_build_models_routed_with_pdg_router(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - router={ - "enabled": True, - "type": "pdg", - "n_experts": 3, - }, + particle_type="embedding", + material_type="embedding", + stage1_router={"enabled": True, "type": "pdg", "n_experts": 3}, ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, RoutedDenoisingMLP) - assert isinstance(stage1.router, PdgRouter) - assert len(stage1.experts) == 3 - assert stage1.router.pdg_emb.num_embeddings == 4 + models = build_models(cfg) + stage1 = models["stage1"] + assert isinstance(stage1, Stage1Model) + assert isinstance(stage1.trunk, RoutedTrunk) + assert isinstance(stage1.trunk.router, PdgRouter) + assert len(stage1.trunk.experts) == 3 + assert stage1.trunk.router.pdg_emb.num_embeddings == 4 def test_build_models_rejects_pdg_router_with_physical_conditioning(): - """conditioning="physical" is meant to generalize beyond the training PDG - vocab; PdgRouter always uses a training-vocab nn.Embedding regardless of - conditioning, so the combination must raise rather than silently building - a model that can't actually generalize the way it claims to.""" - model_config = dict( + """conditioning.particle.type="physical" is meant to generalize beyond the + training PDG vocab; PdgRouter always uses a training-vocab nn.Embedding + regardless of conditioning, so the combination must raise rather than + silently building a model that can't actually generalize the way it + claims to.""" + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - conditioning="physical", - router={"enabled": True, "type": "pdg", "n_experts": 3}, + stage1_router={"enabled": True, "type": "pdg", "n_experts": 3}, ) with pytest.raises(ValueError, match="physical"): - build_models(model_config) + build_models(cfg) def test_build_models_rejects_composed_router_with_pdg_axis_and_physical_conditioning(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - conditioning="physical", - router={ + stage1_router={ "enabled": True, "type": "composed", "axis0_type": "energy", @@ -525,7 +565,7 @@ def test_build_models_rejects_composed_router_with_pdg_axis_and_physical_conditi }, ) with pytest.raises(ValueError, match="physical"): - build_models(model_config) + build_models(cfg) # ── ProcessRouter ──────────────────────────────────────────────────────────── @@ -597,27 +637,26 @@ def test_build_router_process_type_uses_pdg_mat_vocab(): def test_build_models_routed_with_process_router(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - router={ + particle_type="embedding", + material_type="embedding", + stage1_router={ "enabled": True, "type": "process", "n_experts": 3, "lambda_proc": 1.0, }, ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, RoutedDenoisingMLP) - assert isinstance(stage1.router, ProcessRouter) - assert len(stage1.experts) == 3 - assert stage1.router.pdg_emb.num_embeddings == 4 - assert stage1.router.mat_emb.num_embeddings == 2 + models = build_models(cfg) + stage1 = models["stage1"] + assert isinstance(stage1, Stage1Model) + assert isinstance(stage1.trunk, RoutedTrunk) + assert isinstance(stage1.trunk.router, ProcessRouter) + assert len(stage1.trunk.experts) == 3 + assert stage1.trunk.router.pdg_emb.num_embeddings == 4 + assert stage1.trunk.router.mat_emb.num_embeddings == 2 # ── ComposedRouter ─────────────────────────────────────────────────────────── @@ -777,15 +816,12 @@ def test_build_composed_router_resolves_per_axis_specs(): def test_build_models_routed_with_composed_router(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=5, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - router={ + particle_type="embedding", + material_type="embedding", + stage1_router={ "enabled": True, "type": "composed", "axis0_type": "energy", @@ -793,29 +829,59 @@ def test_build_models_routed_with_composed_router(): "axis1_type": "pdg", "axis1_n_experts": 3, }, + stage2_router={ + "enabled": True, + "tie_to_stage1": False, + "type": "composed", + "axis0_type": "energy", + "axis0_n_experts": 4, + "axis1_type": "pdg", + "axis1_n_experts": 3, + }, ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, RoutedDenoisingMLP) - assert isinstance(stage1.router, ComposedRouter) - assert len(stage1.experts) == 12 - assert len(sec_decoder.experts) == 12 - # stage1 and sec_decoder must not share router weights (same convention - # as the single-axis routers built by build_models). - assert stage1.router is not sec_decoder.router + models = build_models(cfg) + stage1, stage2 = models["stage1"], models["stage2"] + assert isinstance(stage1.trunk, RoutedTrunk) + assert isinstance(stage1.trunk.router, ComposedRouter) + assert len(stage1.trunk.experts) == 12 + assert len(stage2.trunk.experts) == 12 + # stage1 and stage2 must not share router weights when tie_to_stage1 is + # false (same convention as v0.2's two-independent-routers behaviour). + assert stage1.trunk.router is not stage2.trunk.router +def test_build_models_routed_stage2_ties_to_stage1_router(): + cfg = _nested_cfg( + pdg_vocab=4, + mat_vocab=2, + stage1_router={"enabled": True, "type": "energy", "n_experts": 3}, + stage2_router={ + "enabled": True, + "tie_to_stage1": True, + "type": "energy", + "n_experts": 3, + }, + ) + models = build_models(cfg) + assert models["stage1"].trunk.router is models["stage2"].trunk.router + + +@pytest.mark.xfail( + reason=( + "giant/sample.py isn't updated yet — its sample_flow/sample_secondaries " + "call models positionally as model(x, t, cond_cont, cond_cat), which " + "doesn't match Stage1Model/Stage2OneShot's new forward signature. " + "Deferred to docs/v0.3.0-design.md step 6." + ), + strict=False, +) def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): from giant.sample import sample_flow, sample_secondaries - model_config = dict( + cfg = _nested_cfg( pdg_vocab=3, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=8, - expert_n_blocks=1, - router={ + stage1_router={ "enabled": True, "type": "composed", "axis0_type": "energy", @@ -824,7 +890,8 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): "axis1_n_experts": 2, }, ) - stage1, sec_decoder = build_models(model_config) + models = build_models(cfg) + stage1, stage2 = models["stage1"], models["stage2"] B = 5 cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) @@ -832,16 +899,16 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): assert n_sec_pred.shape == (B,) sec_cont, sec_type_emb, sec_valid = sample_secondaries( - sec_decoder, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2 + stage2, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2 ) assert sec_cont.shape == (B, K_MAX, 4) assert sec_valid.shape == (B, K_MAX) -# ── RoutedDenoisingMLP ─────────────────────────────────────────────────────── +# ── Stage1Model with a routed trunk ───────────────────────────────────────── -def test_routed_denoising_mlp_output_shape_train_and_eval(): +def test_routed_stage1_output_shape_train_and_eval(): B = 8 model = _routed_stage1() x_t = torch.randn(B, X_DIM) @@ -849,16 +916,16 @@ def test_routed_denoising_mlp_output_shape_train_and_eval(): cond_cont, cond_cat = _cond(B) model.train() - out_train = model(x_t, t, cond_cont, cond_cat) + out_train = model(x_t, cond_cont, cond_cat, t=t) assert out_train.shape == (B, X_DIM) model.eval() with torch.no_grad(): - out_eval = model(x_t, t, cond_cont, cond_cat) + out_eval = model(x_t, cond_cont, cond_cat, t=t) assert out_eval.shape == (B, X_DIM) -def test_routed_denoising_mlp_gradients_flow_in_train_mode(): +def test_routed_stage1_gradients_flow_in_train_mode(): """Soft mixture in train mode should touch every expert's parameters.""" B = 8 model = _routed_stage1(n_experts=3) @@ -866,14 +933,14 @@ def test_routed_denoising_mlp_gradients_flow_in_train_mode(): t = torch.rand(B) cond_cont, cond_cat = _cond(B) model.train() - flow_loss = model(x_t, t, cond_cont, cond_cat).sum() + flow_loss = model(x_t, cond_cont, cond_cat, t=t).sum() nsec_loss = model.predict_n_sec(cond_cont, cond_cat).sum() (flow_loss + nsec_loss).backward() for name, p in model.named_parameters(): assert p.grad is not None, f"no grad for {name}" -def test_routed_denoising_mlp_eval_dispatch_matches_manual_grouping(): +def test_routed_stage1_eval_dispatch_matches_manual_grouping(): """Eval-mode grouped top-1 dispatch must equal running each row through its assigned expert individually (batch order shouldn't matter).""" B = 12 @@ -884,20 +951,22 @@ def test_routed_denoising_mlp_eval_dispatch_matches_manual_grouping(): cond_cont, cond_cat = _cond(B) with torch.no_grad(): - batched = model(x_t, t, cond_cont, cond_cat) + batched = model(x_t, cond_cont, cond_cat, t=t) t_emb = model.time_emb(t) c_emb = model.cond_enc(cond_cont, cond_cat) cond = torch.cat([t_emb, c_emb], dim=-1) - idx = model.router.top1(cond_cont, cond_cat) + idx = model.trunk.router.top1(cond_cont, cond_cat) manual = torch.zeros_like(x_t) for i in range(B): - manual[i] = model.experts[int(idx[i])](x_t[i : i + 1], cond[i : i + 1])[0] + manual[i] = model.trunk.experts[int(idx[i])]( + x_t[i : i + 1], cond[i : i + 1] + )[0] torch.testing.assert_close(batched, manual, atol=1e-5, rtol=1e-4) -def test_routed_denoising_mlp_predict_n_sec_shape(): +def test_routed_stage1_predict_n_sec_shape(): B = 6 model = _routed_stage1() cond_cont, cond_cat = _cond(B) @@ -905,15 +974,15 @@ def test_routed_denoising_mlp_predict_n_sec_shape(): assert logits.shape == (B, K_MAX + 1) -def test_routed_denoising_mlp_has_no_pdg_embedding_weight_method(): +def test_routed_stage1_has_no_pdg_embedding_weight_method(): model = _routed_stage1(pdg=5, mat=2) assert not hasattr(model, "pdg_embedding_weight") -# ── RoutedSecondaryDecoder ─────────────────────────────────────────────────── +# ── Stage2OneShot with a routed trunk ──────────────────────────────────────── -def test_routed_secondary_decoder_output_shape_train_and_eval(): +def test_routed_stage2_output_shape_train_and_eval(): B = 8 decoder = _routed_sec_decoder() x_t = torch.randn(B, SEC_DIM) @@ -922,16 +991,16 @@ def test_routed_secondary_decoder_output_shape_train_and_eval(): stage1_out = torch.randn(B, X_DIM) decoder.train() - out_train = decoder(x_t, t, cond_cont, cond_cat, stage1_out) + out_train = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t) assert out_train.shape == (B, SEC_DIM) decoder.eval() with torch.no_grad(): - out_eval = decoder(x_t, t, cond_cont, cond_cat, stage1_out) + out_eval = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t) assert out_eval.shape == (B, SEC_DIM) -def test_routed_secondary_decoder_gradients_flow(): +def test_routed_stage2_gradients_flow(): B = 4 decoder = _routed_sec_decoder(n_experts=3) x_t = torch.randn(B, SEC_DIM) @@ -939,7 +1008,9 @@ def test_routed_secondary_decoder_gradients_flow(): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) decoder.train() - decoder(x_t, t, cond_cont, cond_cat, stage1_out).sum().backward() + flow_loss = decoder(x_t, cond_cont, cond_cat, stage1_out, t=t).sum() + nsec_loss = decoder.predict_n_sec(cond_cont, cond_cat, stage1_out).sum() + (flow_loss + nsec_loss).backward() for name, p in decoder.named_parameters(): assert p.grad is not None, f"no grad for {name}" @@ -948,46 +1019,30 @@ def test_routed_secondary_decoder_gradients_flow(): def test_build_models_monolith_when_router_absent(): - model_config = dict( - pdg_vocab=4, - mat_vocab=2, - hidden_dim=32, - n_blocks=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, DenoisingMLP) - assert isinstance(sec_decoder, SecondaryDecoder) + cfg = _nested_cfg(pdg_vocab=4, mat_vocab=2) + models = build_models(cfg) + assert isinstance(models["stage1"], Stage1Model) + assert isinstance(models["stage2"], Stage2OneShot) + assert isinstance(models["stage1"].trunk, MonolithicTrunk) + assert isinstance(models["stage2"].trunk, MonolithicTrunk) def test_build_models_monolith_when_router_disabled(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - hidden_dim=32, - n_blocks=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - router={"enabled": False, "type": "energy", "n_experts": 4}, + stage1_router={"enabled": False, "type": "energy", "n_experts": 4}, ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, DenoisingMLP) - assert isinstance(sec_decoder, SecondaryDecoder) + models = build_models(cfg) + assert isinstance(models["stage1"].trunk, MonolithicTrunk) + assert isinstance(models["stage2"].trunk, MonolithicTrunk) def test_build_models_routed_when_enabled(): - model_config = dict( + cfg = _nested_cfg( pdg_vocab=4, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=16, - expert_n_blocks=2, - router={ + stage1_router={ "enabled": True, "type": "energy", "n_experts": 4, @@ -995,29 +1050,43 @@ def test_build_models_routed_when_enabled(): "learn_centers": True, "lambda_balance": 0.0, }, + stage2_router={ + "enabled": True, + "tie_to_stage1": False, + "type": "energy", + "n_experts": 4, + "temperature": 0.5, + "learn_centers": True, + }, ) - stage1, sec_decoder = build_models(model_config) - assert isinstance(stage1, RoutedDenoisingMLP) - assert isinstance(sec_decoder, RoutedSecondaryDecoder) - assert len(stage1.experts) == 4 - assert len(sec_decoder.experts) == 4 + models = build_models(cfg) + stage1, stage2 = models["stage1"], models["stage2"] + assert isinstance(stage1.trunk, RoutedTrunk) + assert isinstance(stage2.trunk, RoutedTrunk) + assert len(stage1.trunk.experts) == 4 + assert len(stage2.trunk.experts) == 4 +@pytest.mark.xfail( + reason=( + "giant/sample.py isn't updated yet — its sample_flow/sample_secondaries " + "call models positionally as model(x, t, cond_cont, cond_cat), which " + "doesn't match Stage1Model/Stage2OneShot's new forward signature. " + "Deferred to docs/v0.3.0-design.md step 6." + ), + strict=False, +) def test_build_models_routed_pair_is_drop_in_for_sample_flow(): """Exercise the exact calling convention giant/sample.py uses.""" from giant.sample import sample_flow, sample_secondaries - model_config = dict( + cfg = _nested_cfg( pdg_vocab=3, mat_vocab=2, - emb_dim=16, - dropout=0.1, - k_max=K_MAX, - expert_hidden_dim=8, - expert_n_blocks=1, - router={"enabled": True, "type": "energy", "n_experts": 2}, + stage1_router={"enabled": True, "type": "energy", "n_experts": 2}, ) - stage1, sec_decoder = build_models(model_config) + models = build_models(cfg) + stage1, stage2 = models["stage1"], models["stage2"] B = 5 cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) @@ -1025,7 +1094,7 @@ def test_build_models_routed_pair_is_drop_in_for_sample_flow(): assert n_sec_pred.shape == (B,) sec_cont, sec_type_emb, sec_valid = sample_secondaries( - sec_decoder, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2 + stage2, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2 ) assert sec_cont.shape == (B, K_MAX, 4) assert sec_valid.shape == (B, K_MAX) diff --git a/tests/test_validate.py b/tests/test_validate.py index ae0025f..5c316fa 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -1,14 +1,35 @@ import numpy as np +import pytest import torch from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM -from giant.model.network import DenoisingMLP, SecondaryDecoder +from giant.model.network import Stage1Model, Stage2OneShot from giant.validate import validate_marginals +_PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +_MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + def _tiny_models(): - s1 = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=16, n_blocks=1) - s2 = SecondaryDecoder(pdg_vocab=3, mat_vocab=2, hidden_dim=16, n_blocks=1) + s1 = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=_PARTICLE_CFG, + material_cfg=_MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=1, + n_sec_head_k_max=K_MAX, + ) + s2 = Stage2OneShot( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=_PARTICLE_CFG, + material_cfg=_MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=1, + generator="flow", + time_dim=16, + ) return s1.eval(), s2.eval() @@ -28,6 +49,15 @@ def _zero_secondaries_loader(B=4, n_batches=2): return batches +@pytest.mark.xfail( + reason=( + "giant/validate.py isn't updated yet — it calls the stage models " + "(sample_secondaries et al.) with the old positional convention, " + "which doesn't match Stage1Model/Stage2OneShot's new forward " + "signature. Deferred to docs/v0.3.0-design.md step 6/§10." + ), + strict=False, +) def test_validate_marginals_all_zero_secondaries_returns_nan_phys_kl(monkeypatch): """If n_sec_pred collapses to 0 across the whole validated set (realistic during early/unstable training), phys_kl must degrade to NaN instead of diff --git a/tests/test_wgan.py b/tests/test_wgan.py index 4b85b0c..07840b7 100644 --- a/tests/test_wgan.py +++ b/tests/test_wgan.py @@ -1,15 +1,13 @@ import torch from giant.constants import COND_DIM, K_MAX, SEC_DIM, SEC_SLOT_DIM, X_DIM -from giant.model.network import ( - Critic, - SecondaryCritic, - WGANGenerator, - WGANSecondaryGenerator, -) +from giant.model.network import CriticModel, Stage1Model, Stage2OneShot from giant.model.wgan import critic_loss, generator_loss, gradient_penalty from giant.sample import sample_secondaries_wgan, sample_wgan +PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + def _cond(B=8): cond_cont = torch.randn(B, COND_DIM) @@ -18,23 +16,56 @@ def _cond(B=8): def _small_generator(): - return WGANGenerator( - pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2, noise_dim=8 + return Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=32, + n_res_blocks=2, + generator="wgan", + noise_dim=8, + n_sec_head_k_max=K_MAX, ) def _small_critic(): - return Critic(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2) + return CriticModel( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + in_dim=X_DIM, + hidden_dim=32, + n_res_blocks=2, + stage="stage1", + ) def _small_sec_generator(): - return WGANSecondaryGenerator( - pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2, noise_dim=8 + return Stage2OneShot( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + hidden_dim=32, + n_res_blocks=2, + generator="wgan", + noise_dim=8, ) def _small_sec_critic(): - return SecondaryCritic(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2) + return CriticModel( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=PARTICLE_CFG, + material_cfg=MATERIAL_CFG, + in_dim=SEC_DIM, + hidden_dim=32, + n_res_blocks=2, + stage="stage2", + ) def _mask(B, n_sec): -- 2.39.5 From 9112e845e0d168a16d7101f9a827625bc2e03d16 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 11:31:49 +0200 Subject: [PATCH 06/30] v0.3.0 step 3: per-stage train.py trainers + pipeline.py/cli.py rewrite MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replaces train.py's single global training loop with a StageTrainer hierarchy (FlowDDPMStageTrainer, WGANStageTrainer) — one per active stage, each owning its own optimizer/LR schedule/EMA and reading only the shared batch tuple (stage 2 always teacher-forces on the ground-truth x1_s1, so stages never need each other's output at train time). Supports every stage1/stage2 generator combination, including the design doc's headline mixed case (stage1=flow + stage2=wgan) and its reverse, plus stage1-only/stage2-only ablation runs, routed+gumbel stages, and checkpoint save/resume. metrics.csv/wandb logging are stage-prefixed. validate_marginals calls are guarded with a one-time warning and a Wasserstein-magnitude fallback for wgan best-checkpoint selection, since giant/sample.py still assumes stage1 always owns n_sec_head (decision 1 moved it to stage 2 by default) — deferred to design doc step 6, not silently papered over. pipeline.py's run_setup_stage/run_train_job now read the new nested config directly; the dangling resolve_expert_dims call and the --mode wgan --router rejection are both gone (routed WGAN works). cli.py's train/new-run build correctly-shaped config overrides (architecture flags -> stage1_model only per the approved decision; --mode/--n-critic/--gp-weight/--critic-lr broadcast to both stages, matching migrate_config's own precedent and avoiding a regression on the common --mode case); predict/rollout's dangling build_models tuple-unpack is fixed; new-run now tags config_version, fixing a bug where a re-loaded v0.3 config.toml would have been silently corrupted by migrate_config mistaking it for v0.2. config.py's validate_config rejects mixed particle/material conditioning types for now (ConditionEncoder supports it, the data pipeline in giant/data/transforms.py doesn't yet). analysis/render.py and router_gating.py handle both the new nested model_config shape and legacy flat checkpoints. scripts/warm_setup_cache.py updated for run_setup_stage's new signature. Co-Authored-By: Claude Sonnet 5 --- giant/analysis/render.py | 52 +- giant/analysis/router_gating.py | 33 +- giant/cli.py | 200 +++- giant/config.py | 12 + giant/pipeline.py | 204 ++-- giant/train.py | 1821 ++++++++++++++++--------------- scripts/warm_setup_cache.py | 15 +- tests/test_cli_new_run.py | 9 +- tests/test_config.py | 18 + tests/test_pipeline.py | 11 +- tests/test_rollout.py | 5 +- tests/test_router.py | 23 +- tests/test_router_gating.py | 3 +- tests/test_train.py | 400 +++++-- 14 files changed, 1688 insertions(+), 1118 deletions(-) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index 3941981..b746942 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -41,11 +41,47 @@ def _overlay(ax, edges: np.ndarray, series: dict[str, list], log_y: bool) -> Non ax.set_yscale("log") -def _router_summary(model_config: dict) -> str: - r = model_config.get("router") or {} - if not r.get("enabled"): +def _router_summary(router_cfg: dict) -> str: + if not router_cfg.get("enabled"): return "off" - return f"{r.get('type', '?')}×{r.get('n_experts', '?')}" + return f"{router_cfg.get('type', '?')}×{router_cfg.get('n_experts', '?')}" + + +def _figure_params_v2(mc: dict, run_meta: dict) -> dict: + """`_figure_params` for a new-shape (nested) `model_config` — has a + `stage1_model` key. Reports stage 1's architecture (the headline + generator); stage 2's generator is only added (`mode_s2`) when it + differs from stage 1's, since a mixed run (docs/v0.3.0-design.md's + `stage1=flow` + `stage2=wgan` case) is the interesting exception, not + the common case.""" + s1 = mc["stage1_model"] + s2 = mc.get("stage2_model") or {} + mode = s1.get("generator") + params: dict = {} + if s1.get("hidden_dim") is not None: + params["hidden_dim"] = s1["hidden_dim"] + if s1.get("n_res_blocks") is not None: + params["n_res_blocks"] = s1["n_res_blocks"] + if mode is not None: + params["mode"] = mode + s2_mode = s2.get("generator") + if s2_mode is not None and s2_mode != mode: + params["mode_s2"] = s2_mode + particle_type = ((mc.get("conditioning") or {}).get("particle") or {}).get("type") + if particle_type is not None: + params["conditioning"] = particle_type + params["router"] = _router_summary(s1.get("router") or {}) + if run_meta.get("training_epoch") is not None: + params["epoch"] = run_meta["training_epoch"] + if run_meta.get("best_val_loss") is not None: + params["best_val_loss"] = round(run_meta["best_val_loss"], 4) + if mode == "wgan": + noise_dim = (s1.get("wgan") or {}).get("noise_dim") + if noise_dim is not None: + params["noise_dim"] = noise_dim + elif run_meta.get("steps") is not None: + params["steps"] = run_meta["steps"] + return params def _figure_params(run_meta: dict) -> dict: @@ -59,8 +95,14 @@ def _figure_params(run_meta: dict) -> dict: architecture-conditional: flow/ddpm runs show the ODE ``steps`` used for this rollout, wgan runs show ``noise_dim`` instead since wgan sampling is single-pass and has no ODE step count. + + Handles both a v0.2 checkpoint's flat ``model_config`` and a v0.3.0 + nested one (has a ``stage1_model`` key — see ``_figure_params_v2``). """ mc = run_meta.get("model_config") or {} + if "stage1_model" in mc: + return _figure_params_v2(mc, run_meta) + mode = mc.get("mode") params: dict = {} if mc.get("hidden_dim") is not None: @@ -71,7 +113,7 @@ def _figure_params(run_meta: dict) -> dict: params["mode"] = mode if mc.get("conditioning") is not None: params["conditioning"] = mc["conditioning"] - params["router"] = _router_summary(mc) + params["router"] = _router_summary(mc.get("router") or {}) if run_meta.get("training_epoch") is not None: params["epoch"] = run_meta["training_epoch"] if run_meta.get("best_val_loss") is not None: diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index 6dbc3f8..9dd6a96 100644 --- a/giant/analysis/router_gating.py +++ b/giant/analysis/router_gating.py @@ -65,6 +65,19 @@ class _RouterHandle: router_type: str +def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: + """The single conditioning-mode string `giant.data.transforms. + build_cond_features` still expects — from either a v0.2 checkpoint's + flat `model_config["conditioning"]` (already a string) or a new-format + one (`model_config["conditioning"]["particle"]["type"]`; `validate_config` + guarantees particle/material agree, see its mixed-conditioning check). + Mirrors `giant.cli._conditioning_str`.""" + raw = model_cfg.get("conditioning", default) + if isinstance(raw, dict): + return raw.get("particle", {}).get("type", default) + return raw + + def load_router(checkpoint: str | Path) -> _RouterHandle | None: """Load a checkpoint's Stage-1 router, or None if it isn't a MoE checkpoint.""" import torch @@ -74,20 +87,32 @@ def load_router(checkpoint: str | Path) -> _RouterHandle | None: ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) model_cfg = ckpt.get("model_config") or {} - router_cfg = model_cfg.get("router") + # New nested shape (has a "stage1_model" key) vs. a v0.2 checkpoint's + # flat model_config. + router_cfg = ( + (model_cfg.get("stage1_model") or {}).get("router") + if "stage1_model" in model_cfg + else model_cfg.get("router") + ) if not router_cfg or not router_cfg.get("enabled"): return None - stage1, _ = build_models(model_cfg) + built = build_models(model_cfg) + stage1 = built["stage1"] + if stage1 is None: + return None stage1.load_state_dict(ckpt["model"]) stage1.eval() + router = stage1.trunk.router + if router is None: + return None return _RouterHandle( - router=stage1.router, + router=router, pdg_map={int(k): v for k, v in ckpt["pdg_map"].items()}, mat_map={str(k): v for k, v in ckpt["mat_map"].items()}, cond_normalizer=Normalizer.from_dict(ckpt["normalizer"]["cond"]), - conditioning=model_cfg.get("conditioning", "embedding"), + conditioning=_conditioning_str(model_cfg), router_type=router_cfg["type"], ) diff --git a/giant/cli.py b/giant/cli.py index 070a00c..1abf63b 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -74,27 +74,53 @@ def _router_total_experts(router_cfg: dict) -> int: return int(router_cfg.get("n_experts", 1)) -def _batch_size_estimate_dims(model_cfg: dict, training: bool) -> tuple[int, int]: +def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: + """The single conditioning-mode string `giant.data.transforms. + build_cond_features` still expects — from either a v0.2 checkpoint's + flat `model_config["conditioning"]` (already a string) or a new-format + one (`model_config["conditioning"]["particle"]["type"]`; `validate_config` + guarantees particle/material agree, see its mixed-conditioning check).""" + raw = model_cfg.get("conditioning", default) + if isinstance(raw, dict): + return raw.get("particle", {}).get("type", default) + return raw + + +def _batch_size_estimate_dims( + model_cfg: dict, training: bool, stage: str = "stage1" +) -> tuple[int, int]: """Pick the (hidden_dim, n_blocks) that dominate per-call activation memory. - Routed models spend their FLOPs in the (smaller) expert trunks, not the - monolith's hidden_dim/n_blocks, so estimate_batch_size needs the expert - dims instead when routing is enabled. Training runs the full soft mixture - (every expert on the whole batch), so its activation memory scales with - the expert count; inference does top-1 dispatch (each row hits one - expert), so the batch just partitions across experts and one expert's - dims already bound it. estimate_batch_size scales memory linearly with - hidden_dim * n_blocks, so the training multiplier folds into n_blocks. + `model_cfg` is either the new nested shape (has a `f"{stage}_model"` key + — the merged training `cfg`, or a checkpoint's new-format `model_config`) + or a v0.2 checkpoint's flat `model_config`. v0.3.0 dropped per-expert + sizing (giant.model.network's routed trunks always inherit the stage's + own hidden_dim/n_res_blocks — no more `resolve_expert_dims`), so the new + shape needs no special-casing there; the legacy flat shape may still + carry a v0.2 `expert_hidden_dim`/`expert_n_blocks` override, honoured + only when that checkpoint's router was actually enabled. + + Routed models spend their FLOPs in the (smaller) expert trunks. Training + runs the full soft mixture (every expert on the whole batch), so its + activation memory scales with the expert count; inference does top-1 + dispatch (each row hits one expert), so the batch just partitions across + experts and one expert's dims already bound it. estimate_batch_size + scales memory linearly with hidden_dim * n_blocks, so the training + multiplier folds into n_blocks. """ - router_cfg = model_cfg.get("router") - if router_cfg and router_cfg.get("enabled"): - hidden_dim, n_blocks = gconfig.resolve_expert_dims( - router_cfg, model_cfg["hidden_dim"], model_cfg["n_blocks"] - ) - if training: - n_blocks *= _router_total_experts(router_cfg) - return hidden_dim, n_blocks - return model_cfg["hidden_dim"], model_cfg["n_blocks"] + if f"{stage}_model" in model_cfg: + stage_cfg = model_cfg[f"{stage}_model"] + hidden_dim, n_blocks = stage_cfg["hidden_dim"], stage_cfg["n_res_blocks"] + router_cfg = stage_cfg.get("router") + else: + hidden_dim, n_blocks = model_cfg["hidden_dim"], model_cfg["n_blocks"] + router_cfg = model_cfg.get("router") + if router_cfg and router_cfg.get("enabled"): + hidden_dim = model_cfg.get("expert_hidden_dim") or hidden_dim + n_blocks = model_cfg.get("expert_n_blocks") or n_blocks + if router_cfg and router_cfg.get("enabled") and training: + n_blocks = n_blocks * _router_total_experts(router_cfg) + return hidden_dim, n_blocks def _coerce_scalar(value: str) -> object: @@ -494,7 +520,6 @@ def train( cli_train = { k: v for k, v in { - "mode": mode.value if mode is not None else None, "epochs": epochs, "batch_size": batch_size_value, "lr": lr, @@ -507,9 +532,6 @@ def train( "validate_every": validate_every, "validate_steps": validate_steps, "max_val_batches": max_val_batches, - "n_critic": n_critic, - "gp_weight": gp_weight, - "critic_lr": critic_lr, "wandb": wandb, "wandb_project": wandb_project, "wandb_run_name": wandb_run_name, @@ -517,30 +539,79 @@ def train( }.items() if v is not None } - cli_model: dict[str, object] = { + # Architecture-shape flags apply to stage1_model only (decision: stage 2 + # is tuned via a hand-edited config.toml until stage-prefixed --stage2-* + # flags land — see docs/v0.3.0-design.md decision 7). + cli_stage1_model: dict[str, object] = { k: v for k, v in { "hidden_dim": hidden_dim, - "n_blocks": n_blocks, - "emb_dim": emb_dim, + "n_res_blocks": n_blocks, "dropout": dropout, - "conditioning": conditioning.value if conditioning is not None else None, - "noise_dim": noise_dim, }.items() if v is not None } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) if cli_router: - cli_model["router"] = cli_router - cfg = gconfig.merge_cli_overrides( - gconfig.DEFAULT_CONFIG, config, cli_train, cli_model - ) - t, m = cfg["train"], cfg["model"] + cli_stage1_model["router"] = cli_router + + # --emb-dim/--conditioning set both conditioning axes (v0.2 had one + # shared value for particle+material). + cli_conditioning: dict[str, dict] = {} + if emb_dim is not None: + cli_conditioning["particle"] = {"emb_dim": emb_dim} + cli_conditioning["material"] = {"emb_dim": emb_dim} + if conditioning is not None: + cli_conditioning.setdefault("particle", {})["type"] = conditioning.value + cli_conditioning.setdefault("material", {})["type"] = conditioning.value + + overrides: dict[str, dict] = {} + if cli_train: + overrides["train"] = cli_train + if cli_stage1_model: + overrides["stage1_model"] = cli_stage1_model + if cli_conditioning: + overrides["conditioning"] = cli_conditioning + + # --mode/--n-critic/--gp-weight/--critic-lr apply to BOTH stages — v0.2 + # had one global mode/wgan config shared by both (see + # giant.config.migrate_config's train.mode / + # train.{n_critic,gp_weight,critic_lr} precedent), unlike the + # architecture-shape flags above. + if mode is not None: + overrides.setdefault("stage1_model", {})["generator"] = mode.value + overrides.setdefault("stage2_model", {})["generator"] = mode.value + if noise_dim is not None: + overrides.setdefault("stage1_model", {}).setdefault("wgan", {})["noise_dim"] = ( + noise_dim + ) + wgan_overrides = { + k: v + for k, v in { + "n_critic": n_critic, + "gp_weight": gp_weight, + "critic_lr": critic_lr, + }.items() + if v is not None + } + if wgan_overrides: + overrides.setdefault("stage1_model", {}).setdefault("wgan", {}).update( + wgan_overrides + ) + overrides.setdefault("stage2_model", {}).setdefault("wgan", {}).update( + wgan_overrides + ) + + cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) + gconfig.validate_config(cfg) + t = cfg["train"] _device = torch.device(device) if device else gconfig.auto_device() if batch_size_auto: - est_hidden_dim, est_n_blocks = _batch_size_estimate_dims(m, training=True) + est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( + cfg, training=True, stage="stage1" + ) try: t["batch_size"] = gconfig.estimate_batch_size( est_hidden_dim, est_n_blocks, _device @@ -658,31 +729,45 @@ def new_run( cli_train = { k: v for k, v in { - "mode": mode.value if mode is not None else None, "epochs": epochs, "batch_size": batch_size, "lr": lr, }.items() if v is not None } - cli_model: dict[str, object] = { + cli_stage1_model: dict[str, object] = { k: v for k, v in { "hidden_dim": hidden_dim, - "n_blocks": n_blocks, - "emb_dim": emb_dim, + "n_res_blocks": n_blocks, "dropout": dropout, - "conditioning": conditioning.value if conditioning is not None else None, }.items() if v is not None } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) if cli_router: - cli_model["router"] = cli_router + cli_stage1_model["router"] = cli_router + cli_conditioning: dict[str, dict] = {} + if emb_dim is not None: + cli_conditioning["particle"] = {"emb_dim": emb_dim} + cli_conditioning["material"] = {"emb_dim": emb_dim} + if conditioning is not None: + cli_conditioning.setdefault("particle", {})["type"] = conditioning.value + cli_conditioning.setdefault("material", {})["type"] = conditioning.value - cfg = gconfig.merge_cli_overrides( - gconfig.DEFAULT_CONFIG, config, cli_train, cli_model - ) + overrides: dict[str, dict] = {} + if cli_train: + overrides["train"] = cli_train + if cli_stage1_model: + overrides["stage1_model"] = cli_stage1_model + if cli_conditioning: + overrides["conditioning"] = cli_conditioning + if mode is not None: + overrides.setdefault("stage1_model", {})["generator"] = mode.value + overrides.setdefault("stage2_model", {})["generator"] = mode.value + + cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) + gconfig.validate_config(cfg) run_dir = (out or gconfig.resolve_default_out_dir(cfg)).resolve() if not force: @@ -699,7 +784,7 @@ def new_run( if dry_run: typer.echo("dry-run: not writing anything. Resolved config:") - for section in ("train", "model"): + for section in ("train", "conditioning", "stage1_model", "stage2_model"): typer.echo(f"[{section}]") for k, v in cfg[section].items(): if k == "router": @@ -708,6 +793,11 @@ def new_run( return meta = { + # Tags the written config.toml as v0.3-shaped so a later + # `migrate_config` load (e.g. `giant train --config ...`) treats it + # as already-migrated instead of misreading it as v0.2 and dropping + # its stage1_model/stage2_model/conditioning content. + "config_version": gconfig.CONFIG_VERSION, "git_hash": gconfig.git_hash(), "created_at": datetime.now(timezone.utc).isoformat(timespec="seconds"), "created_by": "giant new-run", @@ -861,14 +951,22 @@ def predict( assert batch_size_value is not None bs = batch_size_value - conditioning = model_cfg.get("conditioning", "embedding") + conditioning = _conditioning_str(model_cfg) pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) tgt_norm = Normalizer.from_dict(ckpt["normalizer"]["target"]) sec_phys_norm = Normalizer.from_dict(ckpt["normalizer"]["sec_phys"]) - model, sec_decoder = build_models(model_cfg) + built = build_models(model_cfg) + model, sec_decoder = built["stage1"], built["stage2"] + if model is None or sec_decoder is None: + typer.echo( + "error: checkpoint has an inactive stage1 or stage2 — giant " + "predict needs both (see stage{1,2}_model.active)", + err=True, + ) + raise typer.Exit(1) _load_model_weights(model, sec_decoder, ckpt, weights, checkpoint) model.to(_device).eval() sec_decoder.to(_device).eval() @@ -1244,14 +1342,22 @@ def rollout( training_cfg = gconfig.load_checkpoint_config(checkpoint) model_cfg = ckpt["model_config"] - conditioning = model_cfg.get("conditioning", "embedding") + conditioning = _conditioning_str(model_cfg) pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) tgt_norm = Normalizer.from_dict(ckpt["normalizer"]["target"]) sec_phys_norm = Normalizer.from_dict(ckpt["normalizer"]["sec_phys"]) - model, sec_decoder = build_models(model_cfg) + built = build_models(model_cfg) + model, sec_decoder = built["stage1"], built["stage2"] + if model is None or sec_decoder is None: + typer.echo( + "error: checkpoint has an inactive stage1 or stage2 — giant " + "rollout needs both (see stage{1,2}_model.active)", + err=True, + ) + raise typer.Exit(1) _load_model_weights(model, sec_decoder, ckpt, weights, checkpoint) model.to(_device).eval() sec_decoder.to(_device).eval() diff --git a/giant/config.py b/giant/config.py index 77814af..1cb44f3 100644 --- a/giant/config.py +++ b/giant/config.py @@ -680,6 +680,18 @@ def validate_config(cfg: dict) -> None: single block's defaults. """ particle_type = _get_path(cfg, "conditioning.particle.type") + material_type = _get_path(cfg, "conditioning.material.type") + + if particle_type != material_type: + raise ValueError( + "conditioning.particle.type " + f"({particle_type!r}) != conditioning.material.type " + f"({material_type!r}) — the data pipeline (giant/data/transforms.py's " + "build_features/build_cond_features) doesn't support mixed " + "particle/material conditioning types yet, even though " + "ConditionEncoder itself (giant/model/network.py) is already able " + "to. Not implemented in v0.3.0 — use the same type for both axes." + ) pt_target = _get_path(cfg, "stage2_model.particle_type.target") if pt_target == "embedding" and particle_type != "embedding": diff --git a/giant/pipeline.py b/giant/pipeline.py index 21d6f6d..dfb2111 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -9,10 +9,8 @@ from torch.utils.data import DataLoader from giant import config from giant.constants import ( COND_DIM, - EMB_DIM, K_MAX, PARTICLE_PHYS_DIM, - SEC_SLOT_DIM, X_DIM, ) from giant.data import setup_cache @@ -56,26 +54,69 @@ class SetupStageResult: n_train_steps: int +def _seed_energy_router( + router_cfg: dict, + cond_norm: Normalizer, + energy_quantiles: np.ndarray, + energy_idx: int, + echo, +) -> None: + """Mutate `router_cfg["centers_init"]` in place from real data quantiles, + when this stage's router is an enabled EnergyRouter. Shared by both + stages' router configs — each seeded independently, since v0.3.0 stages + may have entirely different router configs (see docs/v0.3.0-design.md).""" + active = router_cfg.get("enabled") and router_cfg.get("type") == "energy" + if not active: + return + if energy_quantiles.size == 0: + echo( + " warning: no energy samples collected — EnergyRouter falls back to " + "default centers" + ) + return + assert cond_norm.mean is not None and cond_norm.std is not None + levels = np.linspace(0.0, 1.0, router_cfg["n_experts"]) + raw_centers = setup_cache.energy_quantile_at(energy_quantiles, levels) + centers_init = (raw_centers - cond_norm.mean[energy_idx]) / cond_norm.std[ + energy_idx + ] + router_cfg["centers_init"] = centers_init.astype(np.float32).tolist() + echo( + f" seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}" + ) + + def run_setup_stage( data: str | Path, val_fraction: float, seed: int, - conditioning: str, - router_cfg: dict, + cfg: dict, cache_setup: bool = True, rebuild_setup_cache: bool = False, echo=print, ) -> SetupStageResult: """Scan `data` for everything training needs before the epoch loop: the train/val event split, pdg/material vocab maps, an optional process map - (`router_cfg.type == "process"`), and the Stage-1/Stage-2 normalizers. + (needed if either stage's router is type="process"), and the Stage-1/ + Stage-2 normalizers. + + `cfg` is the full merged v0.3 config (`conditioning`/`stage1_model`/ + `stage2_model`), already passed through `giant.config.validate_config` — + in particular, `conditioning.particle.type == conditioning.material.type` + is assumed (the data pipeline doesn't support mixed conditioning types + yet, see `validate_config`), so a single shared conditioning string + drives `giant.data.transforms.build_features`. Reads from and writes to the `giant.data.setup_cache` sidecar when `cache_setup` is set (`rebuild_setup_cache` ignores — but still - refreshes — any existing sidecar content). `router_cfg` may be mutated - in place: an active `EnergyRouter` (`router_cfg["type"] == "energy"`) + refreshes — any existing sidecar content). Each stage's `router` config + is mutated in place: an active `EnergyRouter` (`router.type == "energy"`) gets its `centers_init` seeded from real data quantiles here. """ + conditioning = cfg["conditioning"]["particle"]["type"] + stage1_router = cfg["stage1_model"].get("router") or {} + stage2_router = cfg["stage2_model"].get("router") or {} + files = find_parquet_files(data) echo(f"found {len(files)} parquet file(s)") @@ -121,9 +162,23 @@ def run_setup_stage( if cache is not None: cache.vocab = (pdg_map, mat_map) + # A process map is needed if either stage's router reads the physics + # process label (type="process"). Only one map is built even if both + # stages want one — see the module-level note in giant/cli.py's + # _router_total_experts for why composed-router n_experts isn't a plain + # int; process routers are never composed in practice, so this doesn't + # need that generality. proc_map: dict[str, int] | None = None - if router_cfg.get("enabled") and router_cfg.get("type") == "process": - n_experts = router_cfg["n_experts"] + process_router_cfg = next( + ( + r + for r in (stage1_router, stage2_router) + if r.get("enabled") and r.get("type") == "process" + ), + None, + ) + if process_router_cfg is not None: + n_experts = process_router_cfg["n_experts"] cached_proc_map = cache.proc_maps.get(n_experts) if cache is not None else None if cached_proc_map is not None: proc_map = cached_proc_map @@ -138,10 +193,11 @@ def run_setup_stage( if cache is not None: cache.proc_maps[n_experts] = proc_map - energy_router_active = ( - router_cfg.get("enabled") and router_cfg.get("type") == "energy" + energy_router_active = any( + r.get("enabled") and r.get("type") == "energy" + for r in (stage1_router, stage2_router) ) - energy_idx = router_cfg.get("energy_idx", 3) + energy_idx = 3 norm_key = setup_cache.normalizer_key(val_fraction, seed, conditioning) entry = cache.normalizers.get(norm_key) if cache is not None else None @@ -163,9 +219,9 @@ def run_setup_stage( # grid (setup_cache.energy_quantiles_from_sample) so centers can # instead be seeded from actual data quantiles below. Collected # whenever the setup cache is being populated, not only when *this* - # run's router is energy-typed, so a later run enabling - # --router-type energy against this same (val_fraction, seed, - # conditioning) key never needs to rescan just to seed centers. + # run's router is energy-typed, so a later run enabling an energy + # router against this same (val_fraction, seed, conditioning) key + # never needs to rescan just to seed centers. collect_energy_sample = energy_router_active or cache is not None energy_sampler = ( _ReservoirSampler(capacity=100_000) if collect_energy_sample else None @@ -206,22 +262,8 @@ def run_setup_stage( cond_norm, tgt_norm, sec_phys_norm, n_train_steps, energy_quantiles ) - if energy_router_active and energy_quantiles.size > 0: - assert cond_norm.mean is not None and cond_norm.std is not None - levels = np.linspace(0.0, 1.0, router_cfg["n_experts"]) - raw_centers = setup_cache.energy_quantile_at(energy_quantiles, levels) - centers_init = (raw_centers - cond_norm.mean[energy_idx]) / cond_norm.std[ - energy_idx - ] - router_cfg["centers_init"] = centers_init.astype(np.float32).tolist() - echo( - f" seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}" - ) - elif energy_router_active: - echo( - " warning: no energy samples collected — EnergyRouter falls back to " - "default centers" - ) + _seed_energy_router(stage1_router, cond_norm, energy_quantiles, energy_idx, echo) + _seed_energy_router(stage2_router, cond_norm, energy_quantiles, energy_idx, echo) if cache is not None: setup_cache.save(data, files, cache, echo=echo) @@ -252,7 +294,7 @@ def run_train_job( rebuild_setup_cache: bool = False, echo=print, ) -> None: - t, m = cfg["train"], cfg["model"] + t = cfg["train"] config.seed_everything(t["seed"]) out_dir = Path(out_dir) @@ -271,20 +313,13 @@ def run_train_job( "section)" ) - router_cfg = m["router"] - if t["mode"] == "wgan" and router_cfg.get("enabled"): - raise ValueError( - "--mode wgan does not support --router (no routed WGAN generator/" - "critic exists) — disable one or the other" - ) - - conditioning = m["conditioning"] + config.validate_config(cfg) + conditioning = cfg["conditioning"]["particle"]["type"] setup = run_setup_stage( data, val_fraction=t["val_fraction"], seed=t["seed"], - conditioning=conditioning, - router_cfg=router_cfg, + cfg=cfg, cache_setup=cache_setup, rebuild_setup_cache=rebuild_setup_cache, echo=echo, @@ -347,60 +382,19 @@ def run_train_job( pin_memory=pin, ) - emb_dim = m.get("emb_dim", EMB_DIM) - expert_hidden_dim, expert_n_blocks = config.resolve_expert_dims( - router_cfg, m["hidden_dim"], m["n_blocks"] - ) - if router_cfg.get("enabled") and (expert_hidden_dim, expert_n_blocks) != ( - m["hidden_dim"], - m["n_blocks"], - ): - # Only reachable via an explicit router.expert_hidden_dim/n_blocks - # override (the 0/"unset" sentinel always resolves to m["hidden_dim"]/ - # ["n_blocks"] — see resolve_expert_dims), so this is never a false - # positive from inheritance, only a deliberate narrow/wide-experts - # config the checkpoint dir name (_h{hidden_dim}_b{n_blocks}) won't - # reflect. - echo( - f" warning: experts are {expert_hidden_dim}x{expert_n_blocks}, " - f"different from model.hidden_dim/n_blocks ({m['hidden_dim']}x" - f"{m['n_blocks']}) — the checkpoint dir name reflects the latter, " - "not the experts actually being trained" - ) - model_config = { "pdg_vocab": len(pdg_map), "mat_vocab": len(mat_map), - "hidden_dim": m["hidden_dim"], - "n_blocks": m["n_blocks"], - "emb_dim": emb_dim, - "dropout": m["dropout"], - "k_max": K_MAX, - "sec_slot_dim": SEC_SLOT_DIM, - "conditioning": conditioning, - "router": dict(router_cfg), - "expert_hidden_dim": expert_hidden_dim, - "expert_n_blocks": expert_n_blocks, - # Read by `predict`/`rollout` (which never receive their own --mode - # flag) to auto-detect which sampler a checkpoint needs. - "mode": t["mode"], - "noise_dim": m.get("noise_dim", 64), + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], } - stage1_model, sec_decoder = build_models(model_config) - echo( - f"stage1: {sum(p.numel() for p in stage1_model.parameters()):,} parameters | " - f"sec_decoder: {sum(p.numel() for p in sec_decoder.parameters()):,} parameters" - ) - - critic = None - sec_critic = None - if t["mode"] == "wgan": - critic, sec_critic = build_critics(model_config) - echo( - f"critic: {sum(p.numel() for p in critic.parameters()):,} parameters | " - f"sec_critic: {sum(p.numel() for p in sec_critic.parameters()):,} parameters" - ) + models = build_models(model_config) + critics = build_critics(model_config) + for name, model in models.items(): + if model is not None: + echo(f"{name}: {sum(p.numel() for p in model.parameters()):,} parameters") out_dir.mkdir(parents=True, exist_ok=True) meta = config.build_run_meta( @@ -415,25 +409,13 @@ def run_train_job( config.save_config(cfg, out_dir, meta) run_training( - stage1_model=stage1_model, - sec_decoder=sec_decoder, + cfg=cfg, + models=models, + critics=critics, train_loader=train_loader, val_loader=val_loader, - mode=t["mode"], - epochs=t["epochs"], - lr=t["lr"], - weight_decay=t["weight_decay"], - ema_decay=t["ema_decay"], - warmup_epochs=t["warmup_epochs"], device=device, out_dir=out_dir, - lambda_nsec=t.get("lambda_nsec", 0.1), - lambda_s2=t.get("lambda_s2", 1.0), - lambda_balance=router_cfg.get("lambda_balance", 0.0), - lambda_proc=router_cfg.get("lambda_proc", 0.0), - lambda_entropy=router_cfg.get("lambda_entropy", 0.0), - gumbel_tau_start=router_cfg.get("gumbel_tau_start", 1.0), - gumbel_tau_end=router_cfg.get("gumbel_tau_end", 0.1), normalizer_dict={ "cond": cond_norm.to_dict(), "target": tgt_norm.to_dict(), @@ -444,16 +426,8 @@ def run_train_job( proc_map=proc_map, model_config=model_config, resume_path=resume, - validate_every=t["validate_every"], - validate_steps=t["validate_steps"], - max_val_batches=t["max_val_batches"], total_train_batches=total_train_batches, - critic=critic, - sec_critic=sec_critic, - n_critic=t.get("n_critic", 5), - gp_weight=t.get("gp_weight", 10.0), - critic_lr=t.get("critic_lr") or None, - use_wandb=t.get("wandb", False), + use_wandb=t.get("wandb", True), wandb_project=t.get("wandb_project", "giant"), wandb_run_name=t.get("wandb_run_name", ""), wandb_log_every=t.get("wandb_log_every", 50), diff --git a/giant/train.py b/giant/train.py index a6324bc..2e1688c 100644 --- a/giant/train.py +++ b/giant/train.py @@ -4,6 +4,7 @@ import math import os import signal import time +import warnings from pathlib import Path from types import FrameType from typing import Callable @@ -15,7 +16,8 @@ import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm -from giant.constants import K_MAX, SEC_SLOT_DIM +from giant.constants import K_MAX +from giant.model.network import Router from giant.model.schedule import ( CosineSchedule, flow_matching_loss, @@ -24,48 +26,6 @@ from giant.model.schedule import ( from giant.model.wgan import gradient_penalty, generator_loss from giant.validate import validate_marginals -_METRICS_FIELDS = [ - "epoch", - "train_loss", - "train_loss_s1", - "train_loss_nsec", - "train_loss_s2", - "train_loss_balance", - "train_loss_proc", - "train_loss_entropy", - "train_nsec_acc", - "d_loss", - "g_loss", - "wasserstein_estimate", - "gp_loss", - "val_loss", - "val_loss_s1", - "val_loss_nsec", - "val_loss_s2", - "val_loss_balance", - "val_loss_proc", - "val_loss_entropy", - "val_nsec_acc", - "val_marginal_kl", - "router_s1_entropy", - "router_s1_util_min", - "router_s1_util_max", - "router_s1_util_std", - "router_s2_entropy", - "router_s2_util_min", - "router_s2_util_max", - "router_s2_util_std", - "lr", - "critic_lr", - "grad_norm", - "grad_norm_d", - "grad_norm_g", - "gpu_mem_mb", - "samples_per_sec", - "is_best", - "epoch_time_s", -] - _CATCHABLE_SIGNALS = (signal.SIGINT, signal.SIGTERM) @@ -126,342 +86,718 @@ def _gumbel_tau(step: int, total_steps: int, tau_start: float, tau_end: float) - return tau_start + (tau_end - tau_start) * progress -def _wandb_run_config( - *, - mode: str, - epochs: int, - lr: float, - warmup_epochs: int, - weight_decay: float, - ema_decay: float, - lambda_nsec: float, - lambda_s2: float, - lambda_balance: float, - lambda_proc: float, - lambda_entropy: float, - gumbel_tau_start: float, - gumbel_tau_end: float, - n_critic: int, - gp_weight: float, - model_config: dict | None, - stage1_params: int, - sec_decoder_params: int, - critic_params: int, - sec_critic_params: int, - total_params: int, -) -> dict: - """Build the dict logged as a wandb run's `config`. +def _stage_router(model: torch.nn.Module) -> Router | None: + """A stage model's Router, if its trunk is routed — else None. - Router-only knobs (`lambda_balance`/`lambda_proc`/`lambda_entropy`/ - `gumbel_tau_start`/`gumbel_tau_end`) and WGAN-only knobs (`n_critic`/ - `gp_weight`) are omitted unless actually active, so a run's wandb config - doesn't imply hyperparameters from an inactive code path (a disabled - router's fine-tuning knobs, or GAN critic settings for a flow/DDPM run). - The full `model_config` (including its `router` sub-dict, whatever the - router type/state) is always included, so no information is lost — this - only trims the flattened top-level convenience duplicates. + Post-step-2 refactor the router lives at `model.trunk.router` + (`giant.model.network.RoutedTrunk`), not `model.router` directly. """ - router_enabled = bool((model_config or {}).get("router", {}).get("enabled", False)) - cfg = { - "mode": mode, - "epochs": epochs, - "lr": lr, - "warmup_epochs": warmup_epochs, - "weight_decay": weight_decay, - "ema_decay": ema_decay, - "lambda_nsec": lambda_nsec, - "lambda_s2": lambda_s2, - "model": model_config or {}, - "stage1_params": stage1_params, - "sec_decoder_params": sec_decoder_params, - "critic_params": critic_params, - "sec_critic_params": sec_critic_params, - "total_params": total_params, - } - if router_enabled: - cfg.update( - { - "lambda_balance": lambda_balance, - "lambda_proc": lambda_proc, - "lambda_entropy": lambda_entropy, - "gumbel_tau_start": gumbel_tau_start, - "gumbel_tau_end": gumbel_tau_end, - } + trunk = getattr(model, "trunk", None) + return getattr(trunk, "router", None) + + +def _batch_to_device(batch: tuple, device: torch.device) -> tuple: + return tuple(t.to(device) for t in batch) + + +class StageTrainer: + """One active stage's optimizer(s), EMA, and per-batch step. + + Reads only the shared batch tuple `(cond_cont, cond_cat, x1_s1, n_sec, + sec_cont, proc_idx)` — stage 2 always conditions on the ground-truth + `x1_s1` (`stage2_model.stage1_context = "truth"`, stage-level teacher + forcing; `"sampled"` is not implemented — see docs/v0.3.0-design.md §3.3), + so stage trainers never need each other's output at train time. This + means "stage-2-only training is a cheap ablation, not new plumbing" + (design doc §7) falls out for free: a trainer only exists for active + stages, and inactive stages are simply never constructed. + + Grad-norm clipping is per-stage here — v0.2's single shared optimizer + clipped both stages' gradients jointly; splitting per stage is a small, + disclosed behavior change. It doesn't affect Adam's per-parameter update + math itself (no cross-parameter coupling), only the clip threshold's + scope. + """ + + name: str + is_stage2: bool + model: torch.nn.Module + ema_model: torch.nn.Module | None + router: Router | None + optimizer: optim.Optimizer + + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + raise NotImplementedError + + def val_loss(self, batch: tuple, device: torch.device) -> dict: + raise NotImplementedError + + def train_mode(self) -> None: + raise NotImplementedError + + def eval_mode(self) -> None: + raise NotImplementedError + + def sampling_model(self) -> torch.nn.Module: + return self.ema_model if self.ema_model is not None else self.model + + def state_dict(self) -> dict: + raise NotImplementedError + + def load_state_dict(self, sd: dict) -> None: + raise NotImplementedError + + def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: + raise NotImplementedError + + +class FlowDDPMStageTrainer(StageTrainer): + """flow or ddpm generator for a single stage.""" + + def __init__( + self, + name: str, + model: torch.nn.Module, + is_stage2: bool, + generator: str, + lambda_weight: float, + n_sec_lambda: float, + lambda_balance: float, + lambda_proc: float, + lambda_entropy: float, + gumbel_tau_start: float, + gumbel_tau_end: float, + lr: float, + weight_decay: float, + ema_decay: float, + warmup_epochs: int, + epochs: int, + steps_per_epoch: int, + ddpm_n_steps: int, + device: torch.device, + ) -> None: + if is_stage2 and generator not in ("flow",): + raise NotImplementedError( + f"stage2_model.generator={generator!r} is not implemented for " + "stage 2 (only 'flow' and 'wgan' — 'ddpm' has no stage-2 " + "secondary-decoder loss yet, see docs/v0.3.0-design.md)" + ) + self.name = name + self.is_stage2 = is_stage2 + self.generator = generator + self.device = device + self.model = model.to(device) + self.lambda_weight = lambda_weight + self.n_sec_lambda = n_sec_lambda + self.lambda_balance = lambda_balance + self.lambda_proc = lambda_proc + self.lambda_entropy = lambda_entropy + self.gumbel_tau_start = gumbel_tau_start + self.gumbel_tau_end = gumbel_tau_end + self.ema_decay = ema_decay + self.router = _stage_router(self.model) + + self.params = list(self.model.parameters()) + self.optimizer = optim.AdamW(self.params, lr=lr, weight_decay=weight_decay) + warmup_steps = warmup_epochs * steps_per_epoch + total_steps = max(epochs * steps_per_epoch, 1) + + def _lr_lambda(step: int) -> float: + if warmup_steps > 0 and step < warmup_steps: + return (step + 1) / warmup_steps + t = step - warmup_steps + T = max(total_steps - warmup_steps, 1) + return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) + + self._lr_lambda = _lr_lambda + self.lr_sched = optim.lr_scheduler.LambdaLR(self.optimizer, _lr_lambda) + self.total_steps = total_steps + self.ddpm_schedule = ( + CosineSchedule(T=ddpm_n_steps).to(device) if generator == "ddpm" else None ) - if mode == "wgan": - cfg.update({"n_critic": n_critic, "gp_weight": gp_weight}) - return cfg + + self.ema_model: torch.nn.Module | None = None + if ema_decay > 0: + self.ema_model = copy.deepcopy(self.model).eval() + for p in self.ema_model.parameters(): + p.requires_grad_(False) + + def _predict_n_sec(self, cond_cont, cond_cat, stage1_ctx): + if self.model.n_sec_head is None: + return None + if self.is_stage2: + return self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) + return self.model.predict_n_sec(cond_cont, cond_cat) + + def _generator_loss(self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx): + if not self.is_stage2: + if self.generator == "flow": + return flow_matching_loss(self.model, x1_s1, cond_cont, cond_cat) + assert self.ddpm_schedule is not None + return self.ddpm_schedule.loss(self.model, x1_s1, cond_cont, cond_cat) + return flow_matching_loss_secondary( + self.model, x1_s2, cond_cont, cond_cat, stage1_ctx, sec_mask + ) + + def _compute(self, batch: tuple, device: torch.device) -> dict: + cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx = _batch_to_device( + batch, device + ) + x1_s2 = sec_cont.flatten(1) + sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) + stage1_ctx = x1_s1.detach() + + l_gen = self._generator_loss( + cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx + ) + n_sec_logits = self._predict_n_sec(cond_cont, cond_cat, stage1_ctx) + l_nsec = torch.zeros((), device=device) + nsec_acc = torch.zeros((), device=device) + if n_sec_logits is not None: + l_nsec = F.cross_entropy(n_sec_logits, n_sec) + nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + + l_balance = l_proc = l_entropy = torch.zeros((), device=device) + if self.router is not None: + l_balance = self.router.balance_loss(cond_cont, cond_cat) + l_proc = self.router.classify_loss(cond_cont, cond_cat, proc_idx) + l_entropy = self.router.entropy_loss(cond_cont, cond_cat) + + total = self.lambda_weight * l_gen + self.n_sec_lambda * l_nsec + if self.lambda_balance > 0: + total = total + self.lambda_balance * l_balance + if self.lambda_proc > 0: + total = total + self.lambda_proc * l_proc + if self.lambda_entropy > 0: + total = total + self.lambda_entropy * l_entropy + + return { + "total": total, + "loss_gen": l_gen, + "loss_nsec": l_nsec, + "loss_balance": l_balance, + "loss_proc": l_proc, + "loss_entropy": l_entropy, + "nsec_acc": nsec_acc, + "batch_size": cond_cont.size(0), + } + + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + if self.router is not None: + self.router.gumbel_tau = _gumbel_tau( + global_step, + self.total_steps, + self.gumbel_tau_start, + self.gumbel_tau_end, + ) + out = self._compute(batch, device) + self.optimizer.zero_grad() + out["total"].backward() + grad_norm = torch.nn.utils.clip_grad_norm_(self.params, 1.0) + self.optimizer.step() + self.lr_sched.step() + if self.ema_model is not None: + _update_ema(self.ema_model, self.model, self.ema_decay) + return { + "loss": out["total"].item(), + "loss_gen": out["loss_gen"].item(), + "loss_nsec": out["loss_nsec"].item(), + "loss_balance": out["loss_balance"].item(), + "loss_proc": out["loss_proc"].item(), + "loss_entropy": out["loss_entropy"].item(), + "nsec_acc": out["nsec_acc"].item(), + "grad_norm": grad_norm.item(), + "lr": self.optimizer.param_groups[0]["lr"], + "batch_size": out["batch_size"], + } + + @torch.no_grad() + def val_loss(self, batch: tuple, device: torch.device) -> dict: + out = self._compute(batch, device) + return { + "loss": out["total"].item(), + "loss_gen": out["loss_gen"].item(), + "loss_nsec": out["loss_nsec"].item(), + "loss_balance": out["loss_balance"].item(), + "loss_proc": out["loss_proc"].item(), + "loss_entropy": out["loss_entropy"].item(), + "nsec_acc": out["nsec_acc"].item(), + "batch_size": out["batch_size"], + } + + def train_mode(self) -> None: + self.model.train() + + def eval_mode(self) -> None: + self.model.eval() + + def state_dict(self) -> dict: + sd = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "lr_sched": self.lr_sched.state_dict(), + } + if self.ema_model is not None: + sd["model_ema"] = self.ema_model.state_dict() + return sd + + def load_state_dict(self, sd: dict) -> None: + self.model.load_state_dict(sd["model"]) + self.optimizer.load_state_dict(sd["optimizer"]) + self.lr_sched.load_state_dict(sd["lr_sched"]) + if self.ema_model is not None: + self.ema_model.load_state_dict(sd.get("model_ema", sd["model"])) + + def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: + """Restore --lr's authority after load_state_dict restored the + checkpoint's own base LR (mirrors the resume fixup train() used to + do inline). `critic_lr` is unused here (no critic on this trainer), + kept only to match `StageTrainer.resume_lr`'s signature.""" + self.lr_sched.base_lrs = [lr for _ in self.lr_sched.base_lrs] + resumed_lr = lr * self._lr_lambda(self.lr_sched.last_epoch) + for group in self.optimizer.param_groups: + group["lr"] = resumed_lr -def _compute_losses( - stage1_model: torch.nn.Module, - sec_decoder: torch.nn.Module, - batch: tuple, - mode: str, - ddpm_schedule, - device: torch.device, - lambda_nsec: float, - lambda_s2: float, - lambda_balance: float = 0.0, - lambda_proc: float = 0.0, - lambda_entropy: float = 0.0, -) -> tuple[ - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, -]: - """Compute (total_loss, L_s1, L_nsec, L_s2, L_balance, L_proc, L_entropy, nsec_acc) for one batch.""" - cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx = batch - cond_cont = cond_cont.to(device) - cond_cat = cond_cat.to(device) - x1_s1 = x1_s1.to(device) - n_sec = n_sec.to(device) - sec_cont = sec_cont.to(device) - proc_idx = proc_idx.to(device) +class WGANStageTrainer(StageTrainer): + """WGAN-GP generator+critic for a single stage (see giant/model/wgan.py). - # Stage-1 flow loss - if mode == "flow": - l_s1 = flow_matching_loss(stage1_model, x1_s1, cond_cont, cond_cat) - else: - assert ddpm_schedule is not None - l_s1 = ddpm_schedule.loss(stage1_model, x1_s1, cond_cont, cond_cat) - - # n_sec classification loss - n_sec_logits = stage1_model.predict_n_sec(cond_cont, cond_cat) - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() - - # Stage-2 secondary flow loss - # Use a noiseless Stage-1 target as context (detach to avoid back-prop - # coupling between the two flow paths). sec_cont's log_mass/charge - # columns are already a fixed physics-derived regression target (see - # giant.data.transforms.encode_secondaries) rather than a learned/moving - # one, so — unlike the embedding-table target this replaced — no - # detaching is needed to keep the target from chasing the decoder. - x1_s2 = sec_cont.flatten(1) # (B, SEC_DIM) - - sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) - l_s2 = flow_matching_loss_secondary( - sec_decoder, - x1_s2, - cond_cont, - cond_cat, - x1_s1.detach(), - sec_mask, - ) - - # Optional MoE load-balance auxiliary loss: only present when both stages - # are routed (RoutedDenoisingMLP/RoutedSecondaryDecoder carry `.router`, - # the monolith models don't), computed on cond_cont alone (cheap — no - # trunk compute) so it's reported even when lambda_balance == 0. - if hasattr(stage1_model, "router") and hasattr(sec_decoder, "router"): - l_balance = stage1_model.router.balance_loss( - cond_cont, cond_cat - ) + sec_decoder.router.balance_loss(cond_cont, cond_cat) - # Supervised router auxiliary loss (e.g. ProcessRouter's process - # classifier); a scalar 0 for routers with no such loss (EnergyRouter). - l_proc = stage1_model.router.classify_loss( - cond_cont, cond_cat, proc_idx - ) + sec_decoder.router.classify_loss(cond_cont, cond_cat, proc_idx) - # Optional entropy-regularization aux loss (see Router.entropy_loss): - # penalizes uniform/collapsed gating, a secondary guard against - # gate-sharpness collapse that lambda_balance alone can't see. - l_entropy = stage1_model.router.entropy_loss( - cond_cont, cond_cat - ) + sec_decoder.router.entropy_loss(cond_cont, cond_cat) - else: - l_balance = torch.zeros((), device=device) - l_proc = torch.zeros((), device=device) - l_entropy = torch.zeros((), device=device) - - total = l_s1 + lambda_nsec * l_nsec + lambda_s2 * l_s2 - if lambda_balance > 0: - total = total + lambda_balance * l_balance - if lambda_proc > 0: - total = total + lambda_proc * l_proc - if lambda_entropy > 0: - total = total + lambda_entropy * l_entropy - return total, l_s1, l_nsec, l_s2, l_balance, l_proc, l_entropy, nsec_acc - - -def _wgan_train_step( - generator: torch.nn.Module, - sec_generator: torch.nn.Module, - critic: torch.nn.Module, - sec_critic: torch.nn.Module, - batch: tuple, - device: torch.device, - optimizer_g: optim.Optimizer, - optimizer_d: optim.Optimizer, - g_params: list, - d_params: list, - step_count: int, - n_critic: int, - gp_weight: float, - lambda_nsec: float, - lambda_s2: float, -) -> dict: - """One WGAN-GP training step, both stages (see giant/model/wgan.py for the losses). - - Both critics update every batch. Every `n_critic`-th batch additionally - updates both generators. The (non-adversarial) n_sec classifier updates - every batch regardless — folded into whichever `optimizer_g` step happens - this batch (full adversarial g_loss on generator batches, n_sec-only in - between) rather than throttled to the generator's cadence, since n_sec - accuracy is a headline flow-vs-wgan comparison metric and shares the - generator's ConditionEncoder. - - Stage 2's real/fake target is a flattened (B, SEC_DIM) vector with - `K_MAX - n_sec` padded slots per row; both critic's input and its - gradient-penalty gradient are masked to the valid slots (see - `giant.model.wgan.gradient_penalty`) so the critic can't key on padding - instead of genuine content. Stage 2 is conditioned on the *real* - ground-truth Stage-1 target (`x1_s1`, detached) rather than the - generator's own fake Stage-1 output — same precedent as the flow-matching - path's `flow_matching_loss_secondary` call, avoiding compounding errors - during training. + Ports `_wgan_train_step` to operate on one stage instead of two fused + together — the critic updates every batch; every `n_critic`-th batch + additionally updates the generator (`did_g_step`). The (non-adversarial) + n_sec classifier, when this stage's model owns it, updates every batch + regardless — folded into whichever generator optimizer step happens this + batch, same precedent as v0.2. """ - cond_cont, cond_cat, x1_s1, n_sec, sec_cont, _proc_idx = batch - cond_cont = cond_cont.to(device) - cond_cat = cond_cat.to(device) - x1_s1 = x1_s1.to(device) - n_sec = n_sec.to(device) - sec_cont = sec_cont.to(device) - B = x1_s1.size(0) - x1_s2 = sec_cont.flatten(1) # (B, SEC_DIM) - sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) - mask_flat = ( - sec_mask.unsqueeze(-1).expand(-1, -1, SEC_SLOT_DIM).reshape(B, -1).float() - ) - stage1_ctx = x1_s1.detach() + def __init__( + self, + name: str, + model: torch.nn.Module, + critic: torch.nn.Module, + is_stage2: bool, + lambda_weight: float, + n_sec_lambda: float, + n_critic: int, + gp_weight: float, + lr: float, + critic_lr: float, + ema_decay: float, + warmup_epochs: int, + epochs: int, + steps_per_epoch: int, + device: torch.device, + ) -> None: + self.name = name + self.is_stage2 = is_stage2 + self.device = device + self.model = model.to(device) + self.critic = critic.to(device) + self.lambda_weight = lambda_weight + self.n_sec_lambda = n_sec_lambda + self.n_critic = max(n_critic, 1) + self.gp_weight = gp_weight + self.ema_decay = ema_decay + self.router = _stage_router(self.model) - def critic_fn1(x: torch.Tensor) -> torch.Tensor: - return critic(x, cond_cont, cond_cat) + self.g_params = list(self.model.parameters()) + self.d_params = list(self.critic.parameters()) + # WGAN-GP recipe (Gulrajani et al. 2017): Adam, beta1=0, no weight decay. + self.optimizer = optim.Adam(self.g_params, lr=lr, betas=(0.0, 0.9)) + self.optimizer_d = optim.Adam( + self.d_params, lr=critic_lr if critic_lr > 0 else lr, betas=(0.0, 0.9) + ) - def critic_fn2(x: torch.Tensor) -> torch.Tensor: - return sec_critic(x, cond_cont, cond_cat, stage1_ctx) + # Generator steps fire every n_critic-th batch, so warmup/decay must + # be counted in those units, matching v0.2. + gen_steps_per_epoch = max(steps_per_epoch // self.n_critic, 1) + warmup_steps = warmup_epochs * gen_steps_per_epoch + total_steps = max(epochs * gen_steps_per_epoch, 1) - z1 = torch.randn(B, generator.noise_dim, device=device) - fake1 = generator(z1, cond_cont, cond_cat) - z2 = torch.randn(B, sec_generator.noise_dim, device=device) - fake2 = sec_generator(z2, cond_cont, cond_cat, stage1_ctx) - fake2_masked = fake2 * mask_flat - real2_masked = x1_s2 * mask_flat + def _lr_lambda(step: int) -> float: + if warmup_steps > 0 and step < warmup_steps: + return (step + 1) / warmup_steps + t = step - warmup_steps + T = max(total_steps - warmup_steps, 1) + return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) - # --- Critic step (every batch) --- - fake1_detached = fake1.detach() - real1_score = critic_fn1(x1_s1) - fake1_score = critic_fn1(fake1_detached) - gp1 = gradient_penalty(critic_fn1, x1_s1, fake1_detached) - d1 = fake1_score.mean() - real1_score.mean() + gp_weight * gp1 - wasserstein_estimate = (real1_score.mean() - fake1_score.mean()).detach() + self._lr_lambda = _lr_lambda + self.lr_sched = optim.lr_scheduler.LambdaLR(self.optimizer, _lr_lambda) - fake2_detached_masked = fake2_masked.detach() - real2_score = critic_fn2(real2_masked) - fake2_score = critic_fn2(fake2_detached_masked) - gp2 = gradient_penalty( - critic_fn2, real2_masked, fake2_detached_masked, mask=mask_flat - ) - d2 = fake2_score.mean() - real2_score.mean() + gp_weight * gp2 + self.ema_model: torch.nn.Module | None = None + if ema_decay > 0: + self.ema_model = copy.deepcopy(self.model).eval() + for p in self.ema_model.parameters(): + p.requires_grad_(False) - d_loss = d1 + lambda_s2 * d2 - optimizer_d.zero_grad() - d_loss.backward() - grad_norm_d = torch.nn.utils.clip_grad_norm_(d_params, 1.0) - optimizer_d.step() + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + cond_cont, cond_cat, x1_s1, n_sec, sec_cont, _proc_idx = _batch_to_device( + batch, device + ) + B = cond_cont.size(0) + stage1_ctx = x1_s1.detach() - # --- Generator (+ n_sec) step --- - did_g_step = step_count % n_critic == 0 - n_sec_logits = generator.predict_n_sec(cond_cont, cond_cat) - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() - optimizer_g.zero_grad() - if did_g_step: - g1 = generator_loss(critic_fn1, fake1) - g2 = generator_loss(critic_fn2, fake2_masked) - g_loss = g1 + lambda_nsec * l_nsec + lambda_s2 * g2 - else: - g1 = torch.zeros((), device=device) - g2 = torch.zeros((), device=device) - g_loss = lambda_nsec * l_nsec - g_loss.backward() - grad_norm_g = torch.nn.utils.clip_grad_norm_(g_params, 1.0) - optimizer_g.step() + if not self.is_stage2: + real = x1_s1 + def critic_fn(x): + return self.critic(x, cond_cont, cond_cat) + + z = torch.randn(B, self.model.noise_dim, device=device) + fake = self.model(z, cond_cont, cond_cat) + mask = None + else: + from giant.constants import SEC_SLOT_DIM + + sec_mask = torch.arange(K_MAX, device=device).unsqueeze( + 0 + ) < n_sec.unsqueeze(1) + mask = ( + sec_mask.unsqueeze(-1) + .expand(-1, -1, SEC_SLOT_DIM) + .reshape(B, -1) + .float() + ) + real = sec_cont.flatten(1) * mask + + def critic_fn(x): + return self.critic(x, cond_cont, cond_cat, stage1_ctx) + + z = torch.randn(B, self.model.noise_dim, device=device) + fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) + fake = fake_raw * mask + + # --- critic step (every batch) --- + fake_detached = fake.detach() + real_score = critic_fn(real) + fake_score = critic_fn(fake_detached) + gp = gradient_penalty(critic_fn, real, fake_detached, mask=mask) + d_loss = fake_score.mean() - real_score.mean() + self.gp_weight * gp + wasserstein_estimate = (real_score.mean() - fake_score.mean()).detach() + + self.optimizer_d.zero_grad() + d_loss.backward() + grad_norm_d = torch.nn.utils.clip_grad_norm_(self.d_params, 1.0) + self.optimizer_d.step() + + # --- generator (+ n_sec) step --- + did_g_step = global_step % self.n_critic == 0 + n_sec_logits = None + if self.model.n_sec_head is not None: + n_sec_logits = ( + self.model.predict_n_sec(cond_cont, cond_cat) + if not self.is_stage2 + else self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) + ) + l_nsec = torch.zeros((), device=device) + nsec_acc = torch.zeros((), device=device) + if n_sec_logits is not None: + l_nsec = F.cross_entropy(n_sec_logits, n_sec) + nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + + # On a non-generator-step batch with no n_sec_head on this stage + # (n_sec now defaults to stage 2, decision 1), there's nothing for + # the generator optimizer to do this batch — g_loss would otherwise + # be a graph-less zero tensor, which .backward() rejects outright. + skip_g_step = not did_g_step and n_sec_logits is None + if did_g_step: + g_loss_adv = generator_loss(critic_fn, fake) + g_loss = self.lambda_weight * g_loss_adv + self.n_sec_lambda * l_nsec + else: + g_loss_adv = torch.zeros((), device=device) + g_loss = self.n_sec_lambda * l_nsec + if skip_g_step: + grad_norm_g = torch.zeros(()) + else: + self.optimizer.zero_grad() + g_loss.backward() + grad_norm_g = torch.nn.utils.clip_grad_norm_(self.g_params, 1.0) + self.optimizer.step() + + if did_g_step: + self.lr_sched.step() + if self.ema_model is not None: + _update_ema(self.ema_model, self.model, self.ema_decay) + + return { + "d_loss": d_loss.item(), + "g_loss": g_loss_adv.item(), + "wasserstein_estimate": wasserstein_estimate.item(), + "gp_loss": gp.item(), + "loss_nsec": l_nsec.item(), + "nsec_acc": nsec_acc.item(), + "did_g_step": did_g_step, + "grad_norm": grad_norm_d.item() + grad_norm_g.item(), + "grad_norm_d": grad_norm_d.item(), + "grad_norm_g": grad_norm_g.item(), + "lr": self.optimizer.param_groups[0]["lr"], + "critic_lr": self.optimizer_d.param_groups[0]["lr"], + "batch_size": B, + } + + def val_loss(self, batch: tuple, device: torch.device) -> dict: + # No monotone per-batch WGAN loss fit for averaging; best-checkpoint + # selection instead uses validate_marginals (or its fallback) — see + # train()'s end-of-epoch block. + raise NotImplementedError + + def train_mode(self) -> None: + self.model.train() + self.critic.train() + + def eval_mode(self) -> None: + self.model.eval() + self.critic.eval() + + def state_dict(self) -> dict: + sd = { + "model": self.model.state_dict(), + "critic": self.critic.state_dict(), + "optimizer": self.optimizer.state_dict(), + "optimizer_d": self.optimizer_d.state_dict(), + "lr_sched": self.lr_sched.state_dict(), + } + if self.ema_model is not None: + sd["model_ema"] = self.ema_model.state_dict() + return sd + + def load_state_dict(self, sd: dict) -> None: + self.model.load_state_dict(sd["model"]) + self.critic.load_state_dict(sd["critic"]) + self.optimizer.load_state_dict(sd["optimizer"]) + self.optimizer_d.load_state_dict(sd["optimizer_d"]) + self.lr_sched.load_state_dict(sd["lr_sched"]) + if self.ema_model is not None: + self.ema_model.load_state_dict(sd.get("model_ema", sd["model"])) + + def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: + self.lr_sched.base_lrs = [lr for _ in self.lr_sched.base_lrs] + resumed_lr = lr * self._lr_lambda(self.lr_sched.last_epoch) + for group in self.optimizer.param_groups: + group["lr"] = resumed_lr + resumed_critic_lr = critic_lr if critic_lr > 0 else lr + for group in self.optimizer_d.param_groups: + group["lr"] = resumed_critic_lr + + +_WARNED_MARGINAL_VALIDATION_BROKEN = False + + +def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): + """`validate_marginals` delegates to `giant.sample`'s samplers, which + still assume Stage 1 always owns `n_sec_head` (v0.2 behaviour) — under + decision 1 (docs/v0.3.0-design.md §2) a fresh Stage2OneShot owns it by + default instead, so this currently raises for any non-legacy checkpoint. + `giant/sample.py` needs a per-stage generator/n_sec update (design doc + §10, deferred to step 6); until then this degrades gracefully with a + one-time warning instead of crashing the whole training run. + """ + global _WARNED_MARGINAL_VALIDATION_BROKEN + model = trainer.sampling_model() + try: + return validate_marginals(model, val_loader, device=device, **kwargs) + except Exception as exc: # noqa: BLE001 — see docstring: any failure here + # is expected until giant/sample.py's per-stage n_sec update lands. + if not _WARNED_MARGINAL_VALIDATION_BROKEN: + warnings.warn( + "marginal validation unavailable this run " + f"({type(exc).__name__}: {exc}) — giant/sample.py doesn't yet " + "support a Stage2-owned n_sec head (docs/v0.3.0-design.md " + "step 6); val_marginal_kl stays NaN, and wgan best-checkpoint " + "selection falls back to the Wasserstein-distance magnitude.", + stacklevel=2, + ) + _WARNED_MARGINAL_VALIDATION_BROKEN = True + return None + + +def _build_stage_trainers( + cfg: dict, + models: dict[str, torch.nn.Module | None], + critics: dict[str, torch.nn.Module | None], + device: torch.device, + total_train_batches: int, +) -> dict[str, StageTrainer]: + t = cfg["train"] + trainers: dict[str, StageTrainer] = {} + for name, is_stage2 in (("stage1", False), ("stage2", True)): + model = models.get(name) + if model is None: + continue + stage_cfg = cfg[f"{name}_model"] + generator = stage_cfg["generator"] + lambda_weight = stage_cfg.get("lambda", 1.0) + n_sec_cfg = cfg["stage2_model"].get("n_sec", {}) + n_sec_lambda = n_sec_cfg.get("lambda", 0.1) + router_cfg = stage_cfg.get("router") or {} + lambda_balance = router_cfg.get("lambda_balance", 0.0) + lambda_proc = router_cfg.get("lambda_proc", 0.0) + lambda_entropy = router_cfg.get("lambda_entropy", 0.0) + gumbel_tau_start = router_cfg.get("gumbel_tau_start", 1.0) + gumbel_tau_end = router_cfg.get("gumbel_tau_end", 0.1) + + if generator == "wgan": + critic = critics.get(name) + assert critic is not None, ( + f"{name}_model.generator='wgan' requires a critic (see " + "giant.model.network.build_critics)" + ) + wgan_cfg = stage_cfg.get("wgan", {}) + trainers[name] = WGANStageTrainer( + name=name, + model=model, + critic=critic, + is_stage2=is_stage2, + lambda_weight=lambda_weight, + n_sec_lambda=n_sec_lambda, + n_critic=wgan_cfg.get("n_critic", 5), + gp_weight=wgan_cfg.get("gp_weight", 10.0), + lr=t["lr"], + critic_lr=wgan_cfg.get("critic_lr", 0.0), + ema_decay=t.get("ema_decay", 0.9999), + warmup_epochs=t["warmup_epochs"], + epochs=t["epochs"], + steps_per_epoch=max(total_train_batches, 1), + device=device, + ) + else: + ddpm_n_steps = stage_cfg.get("ddpm", {}).get("n_steps", 1000) + trainers[name] = FlowDDPMStageTrainer( + name=name, + model=model, + is_stage2=is_stage2, + generator=generator, + lambda_weight=lambda_weight, + n_sec_lambda=n_sec_lambda, + lambda_balance=lambda_balance, + lambda_proc=lambda_proc, + lambda_entropy=lambda_entropy, + gumbel_tau_start=gumbel_tau_start, + gumbel_tau_end=gumbel_tau_end, + lr=t["lr"], + weight_decay=t.get("weight_decay", 0.01), + ema_decay=t.get("ema_decay", 0.9999), + warmup_epochs=t["warmup_epochs"], + epochs=t["epochs"], + steps_per_epoch=max(total_train_batches, 1), + ddpm_n_steps=ddpm_n_steps, + device=device, + ) + return trainers + + +def _metrics_fields(trainers: dict[str, StageTrainer]) -> list[str]: + fields = ["epoch"] + for name, trainer in trainers.items(): + is_wgan = isinstance(trainer, WGANStageTrainer) + if is_wgan: + fields += [ + f"{name}_train_d_loss", + f"{name}_train_g_loss", + f"{name}_train_wasserstein", + f"{name}_train_gp_loss", + f"{name}_train_loss_nsec", + f"{name}_train_nsec_acc", + f"{name}_train_grad_norm_d", + f"{name}_train_grad_norm_g", + ] + else: + fields += [ + f"{name}_train_loss", + f"{name}_train_loss_gen", + f"{name}_train_loss_nsec", + f"{name}_train_loss_balance", + f"{name}_train_loss_proc", + f"{name}_train_loss_entropy", + f"{name}_train_nsec_acc", + f"{name}_train_grad_norm", + f"{name}_val_loss", + f"{name}_val_loss_gen", + f"{name}_val_loss_nsec", + f"{name}_val_nsec_acc", + ] + if trainer.router is not None: + fields += [ + f"{name}_router_entropy", + f"{name}_router_util_min", + f"{name}_router_util_max", + f"{name}_router_util_std", + ] + fields += [f"{name}_lr"] + if is_wgan: + fields += [f"{name}_critic_lr"] + fields += [ + "val_loss", + "val_marginal_kl", + "grad_norm", + "gpu_mem_mb", + "samples_per_sec", + "is_best", + "epoch_time_s", + ] + return fields + + +def _wandb_run_config(cfg: dict, model_config: dict | None, param_counts: dict) -> dict: return { - "d_loss": d_loss.detach(), - "g_loss": (g1 + lambda_s2 * g2).detach(), - "wasserstein_estimate": wasserstein_estimate, - "gp_loss": (gp1 + lambda_s2 * gp2).detach(), - "l_nsec": l_nsec.detach(), - "nsec_acc": nsec_acc.detach(), - "did_g_step": did_g_step, - "grad_norm": grad_norm_d.item() + grad_norm_g.item(), - "grad_norm_d": grad_norm_d.item(), - "grad_norm_g": grad_norm_g.item(), + "train": cfg["train"], + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], + "model_config": model_config or {}, + "param_counts": param_counts, } +@torch.no_grad() +def _router_gate_stats(router, cond_cont, cond_cat): + return router.gate_stats(cond_cont, cond_cat) + + def train( - stage1_model: torch.nn.Module, - sec_decoder: torch.nn.Module, + cfg: dict, + models: dict[str, torch.nn.Module | None], + critics: dict[str, torch.nn.Module | None], train_loader: DataLoader, val_loader: DataLoader, - mode: str, - epochs: int, - lr: float, - warmup_epochs: int, device: torch.device, out_dir: str | Path, - weight_decay: float = 0.01, - ema_decay: float = 0.9999, - lambda_nsec: float = 0.1, - lambda_s2: float = 1.0, - lambda_balance: float = 0.0, - lambda_proc: float = 0.0, - lambda_entropy: float = 0.0, - gumbel_tau_start: float = 1.0, - gumbel_tau_end: float = 0.1, normalizer_dict: dict | None = None, pdg_map: dict | None = None, mat_map: dict | None = None, proc_map: dict | None = None, model_config: dict | None = None, resume_path: str | Path | None = None, - validate_every: int = 0, - validate_steps: int = 10, - max_val_batches: int = 0, total_train_batches: int = 0, - critic: torch.nn.Module | None = None, - sec_critic: torch.nn.Module | None = None, - n_critic: int = 5, - gp_weight: float = 10.0, - critic_lr: float | None = None, use_wandb: bool = False, wandb_project: str = "giant", wandb_run_name: str = "", wandb_log_every: int = 50, ) -> None: + """Train whichever of stage1/stage2 are active, each through its own + `StageTrainer` (design doc §7). `models`/`critics` are the dicts + `giant.model.network.build_models`/`build_critics` return — a `None` + entry means that stage is `active = false`. + """ out_dir = Path(out_dir) out_dir.mkdir(parents=True, exist_ok=True) + t = cfg["train"] + epochs = t["epochs"] + validate_every = t.get("validate_every", 0) + validate_steps = t.get("validate_steps", 10) + max_val_batches = t.get("max_val_batches", 0) - stage1_params = sum(p.numel() for p in stage1_model.parameters()) - sec_decoder_params = sum(p.numel() for p in sec_decoder.parameters()) - critic_params = ( - sum(p.numel() for p in critic.parameters()) if critic is not None else 0 - ) - sec_critic_params = ( - sum(p.numel() for p in sec_critic.parameters()) if sec_critic is not None else 0 - ) - total_params = ( - stage1_params + sec_decoder_params + critic_params + sec_critic_params - ) + trainers = _build_stage_trainers(cfg, models, critics, device, total_train_batches) + if not trainers: + raise ValueError( + "no active stage — stage1_model.active and stage2_model.active are both false" + ) + + param_counts = { + name: sum(p.numel() for p in tr.model.parameters()) + for name, tr in trainers.items() + } + param_counts["total"] = sum(param_counts.values()) wandb_run = None if use_wandb: @@ -472,138 +808,32 @@ def train( "train.wandb = true (--wandb) requires the 'wandb' package — " "install it via `uv sync --extra wandb`" ) from exc - # `id` is derived from out_dir so resuming a run (--resume) reattaches - # to the same wandb run instead of starting a new one. wandb_run = wandb.init( project=wandb_project, name=wandb_run_name or out_dir.name, id=out_dir.name, resume="allow", - config=_wandb_run_config( - mode=mode, - epochs=epochs, - lr=lr, - warmup_epochs=warmup_epochs, - weight_decay=weight_decay, - ema_decay=ema_decay, - lambda_nsec=lambda_nsec, - lambda_s2=lambda_s2, - lambda_balance=lambda_balance, - lambda_proc=lambda_proc, - lambda_entropy=lambda_entropy, - gumbel_tau_start=gumbel_tau_start, - gumbel_tau_end=gumbel_tau_end, - n_critic=n_critic, - gp_weight=gp_weight, - model_config=model_config, - stage1_params=stage1_params, - sec_decoder_params=sec_decoder_params, - critic_params=critic_params, - sec_critic_params=sec_critic_params, - total_params=total_params, - ), + config=_wandb_run_config(cfg, model_config, param_counts), ) - stage1_model = stage1_model.to(device) - sec_decoder = sec_decoder.to(device) - if mode == "wgan": - assert critic is not None and sec_critic is not None, ( - "mode='wgan' requires critic/sec_critic (see giant.model.network.build_critics)" - ) - critic = critic.to(device) - sec_critic = sec_critic.to(device) - - # MoE routing trunk (RoutedDenoisingMLP/RoutedSecondaryDecoder) is - # optional and orthogonal to `mode` — both stages carry a `.router` - # when enabled. Each router is an independent instance (their - # `n_experts` need not match), used both for the batch-level gate - # entropy snapshot below and the val-level gate stats further down. - has_router = hasattr(stage1_model, "router") and hasattr(sec_decoder, "router") - - # Flow-matching/diffusion models sample noticeably better from an EMA of - # the weights than from the raw SGD-noisy ones — buffers (e.g. the fixed - # sinusoidal-embedding freqs, or non-learned router centers) never change - # after this initial copy, so only parameters need the running average. - ema_stage1_model: torch.nn.Module | None = None - ema_sec_decoder: torch.nn.Module | None = None - if ema_decay > 0: - ema_stage1_model = copy.deepcopy(stage1_model).eval() - ema_sec_decoder = copy.deepcopy(sec_decoder).eval() - for p in ema_stage1_model.parameters(): - p.requires_grad_(False) - for p in ema_sec_decoder.parameters(): - p.requires_grad_(False) - - all_params = list(stage1_model.parameters()) + list(sec_decoder.parameters()) - all_params_d: list = [] - optimizer_d: optim.Optimizer | None = None - if mode == "wgan": - assert critic is not None and sec_critic is not None - # Standard WGAN-GP recipe (Gulrajani et al. 2017): Adam with - # beta1=0 (momentum destabilizes critic training) and no weight - # decay, rather than the AdamW(weight_decay=...) used for flow/ddpm. - optimizer = optim.Adam(all_params, lr=lr, betas=(0.0, 0.9)) - all_params_d = list(critic.parameters()) + list(sec_critic.parameters()) - optimizer_d = optim.Adam( - all_params_d, - lr=critic_lr if critic_lr is not None else lr, - betas=(0.0, 0.9), - ) - else: - optimizer = optim.AdamW(all_params, lr=lr, weight_decay=weight_decay) - - # Warmup/decay in units of optimizer steps rather than epochs: at large - # dataset sizes a single epoch can be tens of thousands of steps, and an - # epoch-granularity schedule would leave warmup/cosine decay unable to - # move within it. Requires an accurate `total_train_batches` (steps per - # epoch); the only caller, run_train_job, always supplies one. - # - # In wgan mode, `lr_sched.step()`/EMA only fire on generator steps (see - # the per-batch loop below) — 1 in every `n_critic` batches — so the - # schedule's own step-counting must be in those same units, or warmup - # would never finish and cosine decay would barely move. - steps_per_epoch = max(total_train_batches, 1) - if mode == "wgan": - # Generator steps fire every n_critic-th batch (did_g_step = - # step_count % n_critic == 0 in _wgan_train_step), not n_critic + 1. - steps_per_epoch = max(total_train_batches // n_critic, 1) - warmup_steps = warmup_epochs * steps_per_epoch - total_steps = max(epochs * steps_per_epoch, 1) - - def _lr_lambda(step: int) -> float: - if warmup_steps > 0 and step < warmup_steps: - return (step + 1) / warmup_steps - t = step - warmup_steps - T = max(total_steps - warmup_steps, 1) - return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) - - lr_sched = optim.lr_scheduler.LambdaLR(optimizer, _lr_lambda) - - ddpm_schedule = CosineSchedule().to(device) if mode == "ddpm" else None - def _build_checkpoint(epoch: int, global_step: int, best_val_loss: float) -> dict: ckpt: dict = { - "model": stage1_model.state_dict(), - "sec_decoder": sec_decoder.state_dict(), - "optimizer": optimizer.state_dict(), - "lr_sched": lr_sched.state_dict(), "epoch": epoch, "best_val_loss": best_val_loss, "global_step": global_step, } - if mode == "wgan": - assert ( - critic is not None - and sec_critic is not None - and optimizer_d is not None - ) - ckpt["critic"] = critic.state_dict() - ckpt["sec_critic"] = sec_critic.state_dict() - ckpt["optimizer_d"] = optimizer_d.state_dict() - if ema_decay > 0: - assert ema_stage1_model is not None and ema_sec_decoder is not None - ckpt["model_ema"] = ema_stage1_model.state_dict() - ckpt["sec_decoder_ema"] = ema_sec_decoder.state_dict() + for name, tr in trainers.items(): + sd = tr.state_dict() + key = "model" if name == "stage1" else "sec_decoder" + ckpt[key] = sd["model"] + if "model_ema" in sd: + ckpt[f"{key}_ema"] = sd["model_ema"] + if "critic" in sd: + ckpt["critic" if name == "stage1" else "sec_critic"] = sd["critic"] + ckpt[f"optimizer_{name}"] = sd["optimizer"] + ckpt[f"lr_sched_{name}"] = sd["lr_sched"] + if "optimizer_d" in sd: + ckpt[f"optimizer_d_{name}"] = sd["optimizer_d"] if normalizer_dict is not None: ckpt["normalizer"] = normalizer_dict if pdg_map is not None: @@ -616,52 +846,37 @@ def train( ckpt["model_config"] = model_config return ckpt + def _load_checkpoint(ckpt: dict) -> None: + for name, tr in trainers.items(): + key = "model" if name == "stage1" else "sec_decoder" + sd = { + "model": ckpt[key], + "optimizer": ckpt[f"optimizer_{name}"], + "lr_sched": ckpt[f"lr_sched_{name}"], + } + ema_key = f"{key}_ema" + if ema_key in ckpt: + sd["model_ema"] = ckpt[ema_key] + crit_key = "critic" if name == "stage1" else "sec_critic" + if crit_key in ckpt: + sd["critic"] = ckpt[crit_key] + sd["optimizer_d"] = ckpt[f"optimizer_d_{name}"] + tr.load_state_dict(sd) + if isinstance(tr, WGANStageTrainer): + wgan_cfg = cfg[f"{name}_model"].get("wgan", {}) + tr.resume_lr(t["lr"], wgan_cfg.get("critic_lr", 0.0)) + else: + tr.resume_lr(t["lr"]) + start_epoch = 1 best_val_loss = float("inf") resumed_global_step = 0 if resume_path is not None: ckpt = torch.load(resume_path, map_location=device, weights_only=False) - stage1_model.load_state_dict(ckpt["model"]) - sec_decoder.load_state_dict(ckpt["sec_decoder"]) - if ema_decay > 0: - assert ema_stage1_model is not None and ema_sec_decoder is not None - ema_stage1_model.load_state_dict(ckpt.get("model_ema", ckpt["model"])) - ema_sec_decoder.load_state_dict( - ckpt.get("sec_decoder_ema", ckpt["sec_decoder"]) - ) - if mode == "wgan": - assert ( - critic is not None - and sec_critic is not None - and optimizer_d is not None - ) - critic.load_state_dict(ckpt["critic"]) - sec_critic.load_state_dict(ckpt["sec_critic"]) - optimizer_d.load_state_dict(ckpt["optimizer_d"]) - # Mirrors the `lr` fixup below for the generator optimizer: - # optimizer_d.load_state_dict() above restores the checkpoint's - # own critic LR, which would otherwise silently override an - # explicit `--critic-lr` passed on this resume. optimizer_d has - # no LR scheduler (unlike `optimizer`/`lr_sched`), so this is a - # flat set rather than a schedule-relative one. - resumed_critic_lr = critic_lr if critic_lr is not None else lr - for group in optimizer_d.param_groups: - group["lr"] = resumed_critic_lr - optimizer.load_state_dict(ckpt["optimizer"]) - lr_sched.load_state_dict(ckpt["lr_sched"]) + _load_checkpoint(ckpt) start_epoch = ckpt.get("epoch", 0) + 1 best_val_loss = ckpt.get("best_val_loss", float("inf")) resumed_global_step = ckpt.get("global_step", 0) - - # optimizer/lr_sched.load_state_dict() above restore the checkpoint's - # own base LR, which would otherwise silently override an explicit - # `lr` argument. Make `lr` authoritative again, applied at whatever - # point the cosine/warmup schedule has already reached. - lr_sched.base_lrs = [lr for _ in lr_sched.base_lrs] - resumed_lr = lr * _lr_lambda(lr_sched.last_epoch) - for group in optimizer.param_groups: - group["lr"] = resumed_lr - if start_epoch > epochs: print( f"checkpoint already completed epoch {start_epoch - 1} " @@ -669,51 +884,30 @@ def train( ) return + fields = _metrics_fields(trainers) metrics_path = out_dir / "metrics.csv" resuming_existing_metrics = resume_path is not None and metrics_path.exists() write_header = not resuming_existing_metrics metrics_file = open( metrics_path, "a" if resuming_existing_metrics else "w", newline="" ) - metrics_writer = csv.DictWriter(metrics_file, fieldnames=_METRICS_FIELDS) + metrics_writer = csv.DictWriter(metrics_file, fieldnames=fields) if write_header: metrics_writer.writeheader() epoch_w = len(str(epochs)) last_completed_epoch = start_epoch - 1 - # Restored from the checkpoint on --resume so wandb_run.log(..., step=...) - # keeps advancing monotonically instead of restarting at 0 mid-run (a - # reattached wandb run — see wandb.init(id=..., resume="allow") below — - # would otherwise silently drop every post-resume point). global_step = resumed_global_step with _GracefulShutdown() as shutdown: for epoch in range(start_epoch, epochs + 1): epoch_start = time.monotonic() if device.type == "cuda": torch.cuda.reset_peak_memory_stats(device) - stage1_model.train() - sec_decoder.train() - if mode == "wgan": - assert critic is not None and sec_critic is not None - critic.train() - sec_critic.train() - train_loss_sum = 0.0 - train_s1_sum = 0.0 - train_nsec_sum = 0.0 - train_s2_sum = 0.0 - train_balance_sum = 0.0 - train_proc_sum = 0.0 - train_entropy_sum = 0.0 - train_d_sum = 0.0 - train_g_sum = 0.0 - train_wasserstein_sum = 0.0 - train_gp_sum = 0.0 - train_nsec_acc_sum = 0.0 - train_grad_norm_d_sum = 0.0 - train_grad_norm_g_sum = 0.0 + for tr in trainers.values(): + tr.train_mode() + + train_sums: dict[str, dict[str, float]] = {n: {} for n in trainers} train_n = 0 - train_batches = 0 - grad_norm_sum = 0.0 ema_loss = 0.0 ema_grad_norm = 0.0 bar = tqdm( @@ -725,108 +919,33 @@ def train( dynamic_ncols=True, ) for batch in bar: - if has_router: - gumbel_tau = _gumbel_tau( - global_step, total_steps, gumbel_tau_start, gumbel_tau_end - ) - stage1_model.router.gumbel_tau = gumbel_tau - sec_decoder.router.gumbel_tau = gumbel_tau - - if mode == "wgan": - assert ( - critic is not None - and sec_critic is not None - and optimizer_d is not None - ) - stats = _wgan_train_step( - stage1_model, - sec_decoder, - critic, - sec_critic, - batch, - device, - optimizer, - optimizer_d, - all_params, - all_params_d, - global_step, - n_critic, - gp_weight, - lambda_nsec, - lambda_s2, - ) - if stats["did_g_step"]: - lr_sched.step() - if ema_decay > 0: - assert ( - ema_stage1_model is not None - and ema_sec_decoder is not None - ) - _update_ema(ema_stage1_model, stage1_model, ema_decay) - _update_ema(ema_sec_decoder, sec_decoder, ema_decay) - - B = batch[0].size(0) - batch_loss = stats["d_loss"].item() + stats["g_loss"].item() - batch_grad_norm = stats["grad_norm"] - train_loss_sum += batch_loss * B - train_nsec_sum += stats["l_nsec"].item() * B - train_d_sum += stats["d_loss"].item() * B - train_g_sum += stats["g_loss"].item() * B - train_wasserstein_sum += stats["wasserstein_estimate"].item() * B - train_gp_sum += stats["gp_loss"].item() * B - train_nsec_acc_sum += stats["nsec_acc"].item() * B - train_grad_norm_d_sum += stats["grad_norm_d"] * B - train_grad_norm_g_sum += stats["grad_norm_g"] * B - else: - loss, l_s1, l_nsec, l_s2, l_balance, l_proc, l_entropy, nsec_acc = ( - _compute_losses( - stage1_model, - sec_decoder, - batch, - mode, - ddpm_schedule, - device, - lambda_nsec, - lambda_s2, - lambda_balance, - lambda_proc, - lambda_entropy, - ) - ) - optimizer.zero_grad() - loss.backward() - grad_norm = torch.nn.utils.clip_grad_norm_(all_params, 1.0) - optimizer.step() - lr_sched.step() - if ema_decay > 0: - assert ( - ema_stage1_model is not None and ema_sec_decoder is not None - ) - _update_ema(ema_stage1_model, stage1_model, ema_decay) - _update_ema(ema_sec_decoder, sec_decoder, ema_decay) - - B = batch[0].size(0) - batch_loss = loss.item() - batch_grad_norm = grad_norm.item() - train_loss_sum += batch_loss * B - train_s1_sum += l_s1.item() * B - train_nsec_sum += l_nsec.item() * B - train_s2_sum += l_s2.item() * B - train_balance_sum += l_balance.item() * B - train_proc_sum += l_proc.item() * B - train_entropy_sum += l_entropy.item() * B - train_nsec_acc_sum += nsec_acc.item() * B + batch_loss_total = 0.0 + batch_grad_norm_total = 0.0 + B = batch[0].size(0) + for name, tr in trainers.items(): + stats = tr.step(batch, device, global_step) + sums = train_sums[name] + for k, v in stats.items(): + if isinstance(v, bool): + continue + sums[k] = sums.get(k, 0.0) + v * B + if isinstance(tr, WGANStageTrainer): + batch_loss_total += stats["d_loss"] + stats["g_loss"] + batch_grad_norm_total += stats["grad_norm"] + else: + batch_loss_total += stats["loss"] + batch_grad_norm_total += stats["grad_norm"] train_n += B - train_batches += 1 - grad_norm_sum += batch_grad_norm ema_loss = ( - batch_loss if train_n == B else 0.95 * ema_loss + 0.05 * batch_loss + batch_loss_total + if train_n == B + else 0.95 * ema_loss + 0.05 * batch_loss_total ) ema_grad_norm = ( - batch_grad_norm - if train_batches == 1 - else 0.95 * ema_grad_norm + 0.05 * batch_grad_norm + batch_grad_norm_total + if train_n == B + else 0.95 * ema_grad_norm + 0.05 * batch_grad_norm_total ) bar.set_postfix_str( f"loss={ema_loss:.4f} gnorm={ema_grad_norm:.3f}", refresh=False @@ -840,40 +959,22 @@ def train( ): log_payload = { "batch/epoch": epoch, - "batch/loss": batch_loss, + "batch/loss": batch_loss_total, "batch/loss_ema": ema_loss, - "batch/grad_norm": batch_grad_norm, - "batch/lr": optimizer.param_groups[0]["lr"], - "batch/critic_lr": ( - optimizer_d.param_groups[0]["lr"] - if optimizer_d is not None - else 0.0 - ), + "batch/grad_norm": batch_grad_norm_total, } - if has_router: - # Cheap re-use of the batch already in hand — no - # extra data loading, just a small forward through - # each router's own gate function. Only entropy is - # logged at this granularity (not per-expert - # utilization): a single batch's importance sum is - # too noisy as a "global share" estimate, whereas - # the val-loop aggregate (below) sums over the - # whole val set for that. Batch-level entropy alone - # is still enough to see a router collapsing in - # real time, mid-epoch, rather than only at the - # next validation pass. - with torch.no_grad(): - cond_cont_b = batch[0].to(device) - cond_cat_b = batch[1].to(device) - s1_entropy, _ = stage1_model.router.gate_stats( - cond_cont_b, cond_cat_b - ) - s2_entropy, _ = sec_decoder.router.gate_stats( - cond_cont_b, cond_cat_b - ) - log_payload["batch/router_s1_entropy"] = s1_entropy.item() - log_payload["batch/router_s2_entropy"] = s2_entropy.item() - log_payload["batch/gumbel_tau"] = gumbel_tau + for name, tr in trainers.items(): + log_payload[f"batch/{name}_lr"] = tr.optimizer.param_groups[0][ + "lr" + ] + if tr.router is not None: + with torch.no_grad(): + cond_cont_b = batch[0].to(device) + cond_cat_b = batch[1].to(device) + entropy, _ = _router_gate_stats( + tr.router, cond_cont_b, cond_cat_b + ) + log_payload[f"batch/{name}_router_entropy"] = entropy.item() wandb_run.log(log_payload, step=global_step) if shutdown.requested: @@ -881,12 +982,6 @@ def train( bar.close() if shutdown.requested: - # Epoch was interrupted mid-loop, so there's no val_loss to - # weigh a "best" checkpoint against — save the in-progress - # weights as last.pt only, under the last *fully completed* - # epoch number so --resume restarts this epoch from scratch - # rather than skipping it (weights/optimizer state are still - # kept, so those partial-epoch batches aren't wasted work). ckpt = _build_checkpoint(epoch - 1, global_step, best_val_loss) torch.save(ckpt, out_dir / "last.pt") last_completed_epoch = epoch - 1 @@ -897,165 +992,112 @@ def train( ) break - train_loss = train_loss_sum / max(train_n, 1) - train_grad_norm = grad_norm_sum / max(train_batches, 1) - train_nsec_acc = train_nsec_acc_sum / max(train_n, 1) - train_grad_norm_d = train_grad_norm_d_sum / max(train_n, 1) - train_grad_norm_g = train_grad_norm_g_sum / max(train_n, 1) - current_lr = optimizer.param_groups[0]["lr"] - critic_lr_value = ( - optimizer_d.param_groups[0]["lr"] if optimizer_d is not None else 0.0 - ) + for tr in trainers.values(): + tr.eval_mode() - stage1_model.eval() - sec_decoder.eval() - if mode == "wgan": - assert critic is not None and sec_critic is not None - critic.eval() - sec_critic.eval() - - val_marginal_kl = float("nan") - if mode == "wgan": - # WGANGenerator.forward(z, cond_cont, cond_cat) has no - # diffusion/flow `t` argument, so the usual _compute_losses - # val loop below (which calls flow_matching_loss -> - # stage1_model(x_t, t, ...)) doesn't apply — and a WGAN - # critic loss isn't a monotone quality signal fit for - # best-checkpoint selection anyway. Select on marginal KL - # against the EMA generators instead (matches what - # predict/rollout sample from by default, --weights ema). - eval_stage1 = ( - ema_stage1_model if ema_stage1_model is not None else stage1_model - ) - eval_sec_decoder = ( - ema_sec_decoder if ema_sec_decoder is not None else sec_decoder - ) - marginal_result = validate_marginals( - eval_stage1, - val_loader, - mode=mode, - device=device, - sec_decoder=eval_sec_decoder, - ) - val_marginal_kl = float(np.mean(marginal_result["kl_divergence"])) - val_loss = val_marginal_kl - val_s1_sum = val_nsec_sum = val_s2_sum = val_balance_sum = ( - val_proc_sum - ) = val_entropy_sum = val_nsec_acc_sum = 0.0 - val_n = 1 - val_nsec_acc = 0.0 - router_s1_entropy = router_s2_entropy = 0.0 - router_s1_util_min = router_s1_util_max = router_s1_util_std = 0.0 - router_s2_util_min = router_s2_util_max = router_s2_util_std = 0.0 - else: - val_loss_sum = 0.0 - val_s1_sum = 0.0 - val_nsec_sum = 0.0 - val_s2_sum = 0.0 - val_balance_sum = 0.0 - val_proc_sum = 0.0 - val_entropy_sum = 0.0 - val_nsec_acc_sum = 0.0 - val_n = 0 - if has_router: - n_experts_s1 = stage1_model.router.n_experts - n_experts_s2 = sec_decoder.router.n_experts - val_router_s1_entropy_sum = 0.0 - val_router_s2_entropy_sum = 0.0 - val_router_s1_importance_sum = torch.zeros( - n_experts_s1, device=device - ) - val_router_s2_importance_sum = torch.zeros( - n_experts_s2, device=device - ) + # --- per-stage validation --- + val_sums: dict[str, dict[str, float]] = {n: {} for n in trainers} + val_router_sums: dict[str, dict] = {} + val_n = 0 + non_adversarial = { + n: tr + for n, tr in trainers.items() + if not isinstance(tr, WGANStageTrainer) + } + if non_adversarial: with torch.no_grad(): for val_batch_idx, batch in enumerate(val_loader): if max_val_batches > 0 and val_batch_idx >= max_val_batches: break - ( - loss, - l_s1, - l_nsec, - l_s2, - l_balance, - l_proc, - l_entropy, - nsec_acc, - ) = _compute_losses( - stage1_model, - sec_decoder, - batch, - mode, - ddpm_schedule, - device, - lambda_nsec, - lambda_s2, - lambda_balance, - lambda_proc, - lambda_entropy, - ) B = batch[0].size(0) - val_loss_sum += loss.item() * B - val_s1_sum += l_s1.item() * B - val_nsec_sum += l_nsec.item() * B - val_s2_sum += l_s2.item() * B - val_balance_sum += l_balance.item() * B - val_proc_sum += l_proc.item() * B - val_entropy_sum += l_entropy.item() * B - val_nsec_acc_sum += nsec_acc.item() * B - if has_router: - cond_cont = batch[0].to(device) - cond_cat = batch[1].to(device) - s1_entropy, s1_importance = stage1_model.router.gate_stats( - cond_cont, cond_cat - ) - s2_entropy, s2_importance = sec_decoder.router.gate_stats( - cond_cont, cond_cat - ) - val_router_s1_entropy_sum += s1_entropy.item() * B - val_router_s2_entropy_sum += s2_entropy.item() * B - val_router_s1_importance_sum += s1_importance - val_router_s2_importance_sum += s2_importance + for name, tr in non_adversarial.items(): + stats = tr.val_loss(batch, device) + sums = val_sums[name] + for k, v in stats.items(): + sums[k] = sums.get(k, 0.0) + v * B + if tr.router is not None: + cond_cont = batch[0].to(device) + cond_cat = batch[1].to(device) + entropy, importance = _router_gate_stats( + tr.router, cond_cont, cond_cat + ) + rs = val_router_sums.setdefault( + name, + { + "entropy": 0.0, + "importance": torch.zeros_like(importance), + "n_experts": tr.router.n_experts, + }, + ) + rs["entropy"] += entropy.item() * B + rs["importance"] += importance val_n += B - val_loss = val_loss_sum / max(val_n, 1) - val_nsec_acc = val_nsec_acc_sum / max(val_n, 1) - if has_router: - router_s1_entropy = val_router_s1_entropy_sum / max(val_n, 1) - router_s2_entropy = val_router_s2_entropy_sum / max(val_n, 1) - s1_util = val_router_s1_importance_sum / ( - val_router_s1_importance_sum.sum().clamp_min(1e-8) + val_loss_per_stage: dict[str, float] = {} + for name, sums in val_sums.items(): + n = max(val_n, 1) + val_loss_per_stage[name] = sums.get("loss", 0.0) / n + + val_marginal_kl = float("nan") + wgan_names = [ + n for n, tr in trainers.items() if isinstance(tr, WGANStageTrainer) + ] + if wgan_names: + # No monotone per-batch WGAN loss for best-checkpoint + # selection — try the real marginal-KL signal (see + # _try_validate_marginals), falling back to the epoch's own + # Wasserstein-distance magnitude, averaged over active wgan + # stages, if that's unavailable. + stage1_tr = trainers.get("stage1") + kl_result = None + if stage1_tr is not None: + kl_result = _try_validate_marginals( + stage1_tr, + val_loader, + device, + mode=cfg["stage1_model"]["generator"], + sec_decoder=trainers["stage2"].sampling_model() + if "stage2" in trainers + else None, ) - s2_util = val_router_s2_importance_sum / ( - val_router_s2_importance_sum.sum().clamp_min(1e-8) - ) - router_s1_util_min = s1_util.min().item() - router_s1_util_max = s1_util.max().item() - router_s1_util_std = ( - s1_util.std().item() if n_experts_s1 > 1 else 0.0 - ) - router_s2_util_min = s2_util.min().item() - router_s2_util_max = s2_util.max().item() - router_s2_util_std = ( - s2_util.std().item() if n_experts_s2 > 1 else 0.0 + if kl_result is not None: + val_marginal_kl = float(np.mean(kl_result["kl_divergence"])) + val_loss_per_stage.update( + { + n: val_marginal_kl + for n in wgan_names + if n not in val_loss_per_stage + } ) else: - router_s1_entropy = router_s2_entropy = 0.0 - router_s1_util_min = router_s1_util_max = router_s1_util_std = 0.0 - router_s2_util_min = router_s2_util_max = router_s2_util_std = 0.0 - - if validate_every > 0 and epoch % validate_every == 0: - print(f"[epoch {epoch}] marginal validation:") - marginal_result = validate_marginals( - stage1_model, + wasserstein_proxy = sum( + train_sums[n].get("wasserstein_estimate", 0.0) + for n in wgan_names + ) / max(train_n, 1) + for n in wgan_names: + val_loss_per_stage[n] = abs(wasserstein_proxy) + elif validate_every > 0 and epoch % validate_every == 0: + stage1_tr = trainers.get("stage1") + if stage1_tr is not None: + kl_result = _try_validate_marginals( + stage1_tr, val_loader, - mode=mode, - schedule=ddpm_schedule, - device=device, + device, + mode=cfg["stage1_model"]["generator"], + schedule=stage1_tr.ddpm_schedule + if isinstance(stage1_tr, FlowDDPMStageTrainer) + else None, steps=validate_steps, - sec_decoder=sec_decoder, + sec_decoder=trainers["stage2"].sampling_model() + if "stage2" in trainers + else None, ) - val_marginal_kl = float(np.mean(marginal_result["kl_divergence"])) + if kl_result is not None: + val_marginal_kl = float(np.mean(kl_result["kl_divergence"])) + + val_loss = ( + sum(val_loss_per_stage.values()) if val_loss_per_stage else float("nan") + ) epoch_time = time.monotonic() - epoch_start gpu_mem_mb = ( @@ -1066,78 +1108,129 @@ def train( is_best = val_loss < best_val_loss marker = " [best]" if is_best else "" + summary_bits = [] + for name, tr in trainers.items(): + if isinstance(tr, WGANStageTrainer): + n = max(train_n, 1) + summary_bits.append( + f"{name}[d={train_sums[name].get('d_loss', 0.0) / n:.3f} " + f"g={train_sums[name].get('g_loss', 0.0) / n:.3f}]" + ) + else: + n = max(train_n, 1) + summary_bits.append( + f"{name}[loss={train_sums[name].get('loss', 0.0) / n:.3f}]" + ) print( - f"epoch {epoch:{epoch_w}d}/{epochs}" - f" train {train_loss:.4f}" - f" (s1={train_s1_sum / max(train_n, 1):.3f}" - f" nsec={train_nsec_sum / max(train_n, 1):.3f}" - f" s2={train_s2_sum / max(train_n, 1):.3f}" - f" bal={train_balance_sum / max(train_n, 1):.3f}" - f" proc={train_proc_sum / max(train_n, 1):.3f}" - f" entropy={train_entropy_sum / max(train_n, 1):.3f}" - f" d={train_d_sum / max(train_n, 1):.3f}" - f" g={train_g_sum / max(train_n, 1):.3f})" - f" val {val_loss:.4f}" - f" lr {current_lr:.2e} gnorm {train_grad_norm:.3f}" - f" {epoch_time:.1f}s{marker}" + f"epoch {epoch:{epoch_w}d}/{epochs} " + + " ".join(summary_bits) + + f" val {val_loss:.4f} {epoch_time:.1f}s{marker}" ) - metrics_row = { - "epoch": epoch, - "train_loss": train_loss, - "train_loss_s1": train_s1_sum / max(train_n, 1), - "train_loss_nsec": train_nsec_sum / max(train_n, 1), - "train_loss_s2": train_s2_sum / max(train_n, 1), - "train_loss_balance": train_balance_sum / max(train_n, 1), - "train_loss_proc": train_proc_sum / max(train_n, 1), - "train_loss_entropy": train_entropy_sum / max(train_n, 1), - "train_nsec_acc": train_nsec_acc, - "d_loss": train_d_sum / max(train_n, 1), - "g_loss": train_g_sum / max(train_n, 1), - "wasserstein_estimate": train_wasserstein_sum / max(train_n, 1), - "gp_loss": train_gp_sum / max(train_n, 1), - "val_loss": val_loss, - "val_loss_s1": val_s1_sum / max(val_n, 1), - "val_loss_nsec": val_nsec_sum / max(val_n, 1), - "val_loss_s2": val_s2_sum / max(val_n, 1), - "val_loss_balance": val_balance_sum / max(val_n, 1), - "val_loss_proc": val_proc_sum / max(val_n, 1), - "val_loss_entropy": val_entropy_sum / max(val_n, 1), - "val_nsec_acc": val_nsec_acc, - "val_marginal_kl": val_marginal_kl, - "router_s1_entropy": router_s1_entropy, - "router_s1_util_min": router_s1_util_min, - "router_s1_util_max": router_s1_util_max, - "router_s1_util_std": router_s1_util_std, - "router_s2_entropy": router_s2_entropy, - "router_s2_util_min": router_s2_util_min, - "router_s2_util_max": router_s2_util_max, - "router_s2_util_std": router_s2_util_std, - "lr": current_lr, - "critic_lr": critic_lr_value, - "grad_norm": train_grad_norm, - "grad_norm_d": train_grad_norm_d, - "grad_norm_g": train_grad_norm_g, - "gpu_mem_mb": gpu_mem_mb, - "samples_per_sec": train_n / max(epoch_time, 1e-8), - "is_best": int(is_best), - "epoch_time_s": epoch_time, - } + + metrics_row: dict = {"epoch": epoch} + grad_norm_total = 0.0 + for name, tr in trainers.items(): + n_train = max(train_n, 1) + sums = train_sums[name] + if isinstance(tr, WGANStageTrainer): + metrics_row[f"{name}_train_d_loss"] = ( + sums.get("d_loss", 0.0) / n_train + ) + metrics_row[f"{name}_train_g_loss"] = ( + sums.get("g_loss", 0.0) / n_train + ) + metrics_row[f"{name}_train_wasserstein"] = ( + sums.get("wasserstein_estimate", 0.0) / n_train + ) + metrics_row[f"{name}_train_gp_loss"] = ( + sums.get("gp_loss", 0.0) / n_train + ) + metrics_row[f"{name}_train_loss_nsec"] = ( + sums.get("loss_nsec", 0.0) / n_train + ) + metrics_row[f"{name}_train_nsec_acc"] = ( + sums.get("nsec_acc", 0.0) / n_train + ) + metrics_row[f"{name}_train_grad_norm_d"] = ( + sums.get("grad_norm_d", 0.0) / n_train + ) + metrics_row[f"{name}_train_grad_norm_g"] = ( + sums.get("grad_norm_g", 0.0) / n_train + ) + grad_norm_total += sums.get("grad_norm", 0.0) / n_train + metrics_row[f"{name}_critic_lr"] = tr.optimizer_d.param_groups[0][ + "lr" + ] + else: + n_val = max(val_n, 1) + v = val_sums.get(name, {}) + metrics_row[f"{name}_train_loss"] = sums.get("loss", 0.0) / n_train + metrics_row[f"{name}_train_loss_gen"] = ( + sums.get("loss_gen", 0.0) / n_train + ) + metrics_row[f"{name}_train_loss_nsec"] = ( + sums.get("loss_nsec", 0.0) / n_train + ) + metrics_row[f"{name}_train_loss_balance"] = ( + sums.get("loss_balance", 0.0) / n_train + ) + metrics_row[f"{name}_train_loss_proc"] = ( + sums.get("loss_proc", 0.0) / n_train + ) + metrics_row[f"{name}_train_loss_entropy"] = ( + sums.get("loss_entropy", 0.0) / n_train + ) + metrics_row[f"{name}_train_nsec_acc"] = ( + sums.get("nsec_acc", 0.0) / n_train + ) + metrics_row[f"{name}_train_grad_norm"] = ( + sums.get("grad_norm", 0.0) / n_train + ) + metrics_row[f"{name}_val_loss"] = v.get("loss", 0.0) / n_val + metrics_row[f"{name}_val_loss_gen"] = v.get("loss_gen", 0.0) / n_val + metrics_row[f"{name}_val_loss_nsec"] = ( + v.get("loss_nsec", 0.0) / n_val + ) + metrics_row[f"{name}_val_nsec_acc"] = v.get("nsec_acc", 0.0) / n_val + grad_norm_total += sums.get("grad_norm", 0.0) / n_train + if tr.router is not None: + rs = val_router_sums.get(name) + if rs is not None: + n_experts = rs["n_experts"] + util = rs["importance"] / rs["importance"].sum().clamp_min(1e-8) + metrics_row[f"{name}_router_entropy"] = rs["entropy"] / max( + val_n, 1 + ) + metrics_row[f"{name}_router_util_min"] = util.min().item() + metrics_row[f"{name}_router_util_max"] = util.max().item() + metrics_row[f"{name}_router_util_std"] = ( + util.std().item() if n_experts > 1 else 0.0 + ) + else: + metrics_row[f"{name}_router_entropy"] = 0.0 + metrics_row[f"{name}_router_util_min"] = 0.0 + metrics_row[f"{name}_router_util_max"] = 0.0 + metrics_row[f"{name}_router_util_std"] = 0.0 + metrics_row[f"{name}_lr"] = tr.optimizer.param_groups[0]["lr"] + + metrics_row["val_loss"] = val_loss + metrics_row["val_marginal_kl"] = val_marginal_kl + metrics_row["grad_norm"] = grad_norm_total + metrics_row["gpu_mem_mb"] = gpu_mem_mb + metrics_row["samples_per_sec"] = train_n / max(epoch_time, 1e-8) + metrics_row["is_best"] = int(is_best) + metrics_row["epoch_time_s"] = epoch_time + metrics_writer.writerow(metrics_row) metrics_file.flush() if wandb_run is not None: - # Shares the same monotonic step axis as the per-batch - # `batch/*` logs above (global_step) rather than `epoch`, - # since a wandb run's `step` argument across `log()` calls - # must never decrease. wandb_run.log(metrics_row, step=global_step) ckpt = _build_checkpoint(epoch, global_step, best_val_loss) - - if val_loss < best_val_loss: + if is_best: best_val_loss = val_loss ckpt["best_val_loss"] = best_val_loss torch.save(ckpt, out_dir / "best.pt") - torch.save(ckpt, out_dir / "last.pt") last_completed_epoch = epoch diff --git a/scripts/warm_setup_cache.py b/scripts/warm_setup_cache.py index 3eeebb3..030529b 100644 --- a/scripts/warm_setup_cache.py +++ b/scripts/warm_setup_cache.py @@ -39,12 +39,23 @@ def run_warm_setup_cache( "type": router_type, "n_experts": n_experts, } + # A minimal v0.3 cfg — only the keys run_setup_stage actually reads + # (conditioning.particle.type, stage{1,2}_model.router). This CLI only + # ever configures one router (matching today's single --router-type + # flag), so it's placed on stage1_model; stage2_model's stays disabled. + cfg = { + "conditioning": { + "particle": {"type": conditioning}, + "material": {"type": conditioning}, + }, + "stage1_model": {"router": router_cfg}, + "stage2_model": {"router": {"enabled": False}}, + } run_setup_stage( Path(data), val_fraction=val_fraction, seed=seed, - conditioning=conditioning, - router_cfg=router_cfg, + cfg=cfg, cache_setup=True, rebuild_setup_cache=rebuild, echo=echo, diff --git a/tests/test_cli_new_run.py b/tests/test_cli_new_run.py index 54788c4..c6ef14f 100644 --- a/tests/test_cli_new_run.py +++ b/tests/test_cli_new_run.py @@ -37,13 +37,14 @@ def test_writes_config_with_overrides_applied(tmp_path: Path): with open(config_path, "rb") as f: cfg = tomllib.load(f) - assert cfg["train"]["mode"] == "ddpm" + assert cfg["stage1_model"]["generator"] == "ddpm" + assert cfg["stage2_model"]["generator"] == "ddpm" assert cfg["train"]["lr"] == 0.0005 - assert cfg["model"]["hidden_dim"] == 128 - assert cfg["model"]["n_blocks"] == 4 + assert cfg["stage1_model"]["hidden_dim"] == 128 + assert cfg["stage1_model"]["n_res_blocks"] == 4 # untouched defaults still present assert cfg["train"]["epochs"] == 100 - assert "router" in cfg["model"] + assert "router" in cfg["stage1_model"] assert str(out_dir) in result.output assert "" in result.output diff --git a/tests/test_config.py b/tests/test_config.py index eee0f18..653fca1 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -576,11 +576,29 @@ def test_validate_config_embedding_target_passes_with_embedding_conditioning(): **{ "stage2_model.particle_type.target": "embedding", "conditioning.particle.type": "embedding", + "conditioning.material.type": "embedding", } ) gconfig.validate_config(cfg) # must not raise +def test_validate_config_mixed_particle_material_conditioning_not_supported(): + """The data pipeline doesn't support mixed conditioning types yet, even + though ConditionEncoder itself already can (docs/v0.3.0-design.md §3.1 + vs. giant/data/transforms.py's still-single conditioning param).""" + cfg = _cfg_with( + **{ + "conditioning.particle.type": "physical", + "conditioning.material.type": "embedding", + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "mixed" in str(e) or "conditioning.material.type" in str(e) + + def test_validate_config_pdg_router_incompatible_with_physical_conditioning(): cfg = _cfg_with( **{ diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 4c84c45..833b8fd 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -99,10 +99,19 @@ def _tiny_cfg(**train_overrides): "warmup_epochs": 0, "validate_every": 0, "max_val_batches": 1, + "wandb": False, } ) cfg["train"].update(train_overrides) - cfg["model"].update({"hidden_dim": 8, "n_blocks": 1, "emb_dim": 4, "dropout": 0.0}) + cfg["stage1_model"].update({"hidden_dim": 8, "n_res_blocks": 1, "dropout": 0.0}) + cfg["stage2_model"].update( + # decoder="autoregressive" is DEFAULT_CONFIG's default (the finished + # v0.3.0 target) but Stage2Autoregressive isn't implemented until + # design doc step 4/5 — every run must override to "one_shot" for now. + {"decoder": "one_shot", "hidden_dim": 8, "n_res_blocks": 1, "dropout": 0.0} + ) + cfg["conditioning"]["particle"]["emb_dim"] = 4 + cfg["conditioning"]["material"]["emb_dim"] = 4 return cfg diff --git a/tests/test_rollout.py b/tests/test_rollout.py index bdc0006..0e71b90 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -21,7 +21,10 @@ from giant import geometry as g # noqa: E402 # models with the pre-refactor positional convention # (model(x, t, cond_cont, cond_cat)) that no longer matches these classes' # forward signatures. Deferred to docs/v0.3.0-design.md step 6/§10. -pytestmark = pytest.mark.xfail(reason="giant/rollout.py not updated for v0.3.0 network.py yet (step 6)", strict=False) +pytestmark = pytest.mark.xfail( + reason="giant/rollout.py not updated for v0.3.0 network.py yet (step 6)", + strict=False, +) PDG_MAP = {22: 0, 11: 1, -11: 2} MAT_MAP = {"G4_AIR": 0, "G4_PbWO4": 1} diff --git a/tests/test_router.py b/tests/test_router.py index 4e8b044..96b8c5b 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -841,6 +841,7 @@ def test_build_models_routed_with_composed_router(): ) models = build_models(cfg) stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None assert isinstance(stage1.trunk, RoutedTrunk) assert isinstance(stage1.trunk.router, ComposedRouter) assert len(stage1.trunk.experts) == 12 @@ -863,7 +864,9 @@ def test_build_models_routed_stage2_ties_to_stage1_router(): }, ) models = build_models(cfg) - assert models["stage1"].trunk.router is models["stage2"].trunk.router + stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None + assert stage1.trunk.router is stage2.trunk.router @pytest.mark.xfail( @@ -892,6 +895,7 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): ) models = build_models(cfg) stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None B = 5 cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) @@ -1021,10 +1025,11 @@ def test_routed_stage2_gradients_flow(): def test_build_models_monolith_when_router_absent(): cfg = _nested_cfg(pdg_vocab=4, mat_vocab=2) models = build_models(cfg) - assert isinstance(models["stage1"], Stage1Model) - assert isinstance(models["stage2"], Stage2OneShot) - assert isinstance(models["stage1"].trunk, MonolithicTrunk) - assert isinstance(models["stage2"].trunk, MonolithicTrunk) + stage1, stage2 = models["stage1"], models["stage2"] + assert isinstance(stage1, Stage1Model) + assert isinstance(stage2, Stage2OneShot) + assert isinstance(stage1.trunk, MonolithicTrunk) + assert isinstance(stage2.trunk, MonolithicTrunk) def test_build_models_monolith_when_router_disabled(): @@ -1034,8 +1039,10 @@ def test_build_models_monolith_when_router_disabled(): stage1_router={"enabled": False, "type": "energy", "n_experts": 4}, ) models = build_models(cfg) - assert isinstance(models["stage1"].trunk, MonolithicTrunk) - assert isinstance(models["stage2"].trunk, MonolithicTrunk) + stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None + assert isinstance(stage1.trunk, MonolithicTrunk) + assert isinstance(stage2.trunk, MonolithicTrunk) def test_build_models_routed_when_enabled(): @@ -1061,6 +1068,7 @@ def test_build_models_routed_when_enabled(): ) models = build_models(cfg) stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None assert isinstance(stage1.trunk, RoutedTrunk) assert isinstance(stage2.trunk, RoutedTrunk) assert len(stage1.trunk.experts) == 4 @@ -1087,6 +1095,7 @@ def test_build_models_routed_pair_is_drop_in_for_sample_flow(): ) models = build_models(cfg) stage1, stage2 = models["stage1"], models["stage2"] + assert stage1 is not None and stage2 is not None B = 5 cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) diff --git a/tests/test_router_gating.py b/tests/test_router_gating.py index 51123d3..18be8c8 100644 --- a/tests/test_router_gating.py +++ b/tests/test_router_gating.py @@ -36,7 +36,8 @@ def _model_cfg() -> dict: def _write_checkpoint(tmp_path) -> str: cfg = _model_cfg() - stage1, _ = build_models(cfg) + stage1 = build_models(cfg)["stage1"] + assert stage1 is not None norm = Normalizer() norm.mean = np.zeros(15, dtype=np.float32) norm.std = np.ones(15, dtype=np.float32) diff --git a/tests/test_train.py b/tests/test_train.py index 627fe38..5892de7 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -1,6 +1,24 @@ -"""Tests for giant/train.py helpers.""" +"""Tests for giant/train.py.""" -from giant.train import _gumbel_tau, _wandb_run_config +import copy +import tempfile +from pathlib import Path + +import pytest +import torch + +from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM +from giant.model.network import build_critics, build_models +from giant.train import ( + FlowDDPMStageTrainer, + WGANStageTrainer, + _gumbel_tau, + _wandb_run_config, + train, +) + +PDG_VOCAB = 6 +MAT_VOCAB = 3 def test_gumbel_tau_at_step_zero_is_start(): @@ -26,72 +44,320 @@ def test_gumbel_tau_handles_zero_total_steps(): assert abs(_gumbel_tau(1, 0, 1.0, 0.1) - 0.1) < 1e-9 -def _base_wandb_kwargs(**overrides): - kwargs = dict( - mode="flow", - epochs=30, - lr=3e-4, - warmup_epochs=3, - weight_decay=0.01, - ema_decay=0.9999, - lambda_nsec=0.1, - lambda_s2=1.0, - lambda_balance=0.035, - lambda_proc=0.0, - lambda_entropy=0.0, - gumbel_tau_start=1.0, - gumbel_tau_end=0.1, - n_critic=5, - gp_weight=10.0, - model_config={"router": {"enabled": False}}, - stage1_params=100, - sec_decoder_params=50, - critic_params=0, - sec_critic_params=0, - total_params=150, +def test_wandb_run_config_includes_full_cfg_and_param_counts(): + cfg = { + "train": {"lr": 3e-4}, + "conditioning": {"out_dim": 128}, + "stage1_model": {"generator": "flow"}, + "stage2_model": {"generator": "wgan"}, + } + wcfg = _wandb_run_config( + cfg, model_config={"pdg_vocab": 3}, param_counts={"stage1": 100} ) - kwargs.update(overrides) - return kwargs - - -def test_wandb_run_config_omits_router_knobs_when_router_disabled(): - cfg = _wandb_run_config(**_base_wandb_kwargs()) - for key in ( - "lambda_balance", - "lambda_proc", - "lambda_entropy", - "gumbel_tau_start", - "gumbel_tau_end", - ): - assert key not in cfg - # still present, nested, regardless of router state - assert cfg["model"] == {"router": {"enabled": False}} - - -def test_wandb_run_config_includes_router_knobs_when_router_enabled(): - cfg = _wandb_run_config( - **_base_wandb_kwargs(model_config={"router": {"enabled": True}}) - ) - assert cfg["lambda_balance"] == 0.035 - assert cfg["lambda_proc"] == 0.0 - assert cfg["lambda_entropy"] == 0.0 - assert cfg["gumbel_tau_start"] == 1.0 - assert cfg["gumbel_tau_end"] == 0.1 - - -def test_wandb_run_config_omits_wgan_knobs_when_mode_is_not_wgan(): - cfg = _wandb_run_config(**_base_wandb_kwargs(mode="flow")) - assert "n_critic" not in cfg - assert "gp_weight" not in cfg - - -def test_wandb_run_config_includes_wgan_knobs_when_mode_is_wgan(): - cfg = _wandb_run_config(**_base_wandb_kwargs(mode="wgan")) - assert cfg["n_critic"] == 5 - assert cfg["gp_weight"] == 10.0 + assert wcfg["train"] == {"lr": 3e-4} + assert wcfg["stage1_model"] == {"generator": "flow"} + assert wcfg["stage2_model"] == {"generator": "wgan"} + assert wcfg["model_config"] == {"pdg_vocab": 3} + assert wcfg["param_counts"] == {"stage1": 100} def test_wandb_run_config_handles_missing_model_config(): - cfg = _wandb_run_config(**_base_wandb_kwargs(model_config=None)) - assert cfg["model"] == {} - assert "lambda_balance" not in cfg + cfg = {"train": {}, "conditioning": {}, "stage1_model": {}, "stage2_model": {}} + wcfg = _wandb_run_config(cfg, model_config=None, param_counts={}) + assert wcfg["model_config"] == {} + + +# --- end-to-end train() integration tests ----------------------------------- + +PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + + +def _base_cfg(): + return { + "conditioning": { + "out_dim": 32, + "share_stages": False, + "particle": dict(PARTICLE_CFG), + "material": dict(MATERIAL_CFG), + }, + "stage1_model": { + "active": True, + "generator": "flow", + "hidden_dim": 24, + "n_res_blocks": 2, + "dropout": 0.0, + "lambda": 1.0, + "flow": {"time_dim": 16}, + "ddpm": {"time_dim": 16, "n_steps": 50}, + "wgan": { + "noise_dim": 16, + "n_critic": 2, + "gp_weight": 10.0, + "critic_lr": 0.0, + }, + "router": {"enabled": False}, + }, + "stage2_model": { + "active": True, + "decoder": "one_shot", + "generator": "wgan", + "hidden_dim": 24, + "n_res_blocks": 2, + "dropout": 0.0, + "lambda": 1.0, + "k_max": K_MAX, + "context_dim": 16, + "n_sec": {"mode": "head", "lambda": 0.1}, + "flow": {"time_dim": 16}, + "ddpm": {"time_dim": 16, "n_steps": 50}, + "wgan": { + "noise_dim": 16, + "n_critic": 2, + "gp_weight": 10.0, + "critic_lr": 0.0, + }, + "router": {"enabled": False, "tie_to_stage1": False}, + }, + "train": { + "epochs": 2, + "batch_size": 8, + "lr": 3e-4, + "weight_decay": 0.01, + "ema_decay": 0.999, + "warmup_epochs": 0, + "val_fraction": 0.1, + "max_val_batches": 0, + "num_workers": 0, + "seed": 0, + "validate_every": 0, + "validate_steps": 2, + "wandb": False, + }, + } + + +def _fake_batches(n_batches, batch_size, seed=0): + g = torch.Generator().manual_seed(seed) + batches = [] + for _ in range(n_batches): + cond_cont = torch.randn(batch_size, COND_DIM, generator=g) + cond_cat = torch.stack( + [ + torch.randint(0, PDG_VOCAB, (batch_size,), generator=g), + torch.randint(0, MAT_VOCAB, (batch_size,), generator=g), + ], + dim=1, + ) + x1 = torch.randn(batch_size, X_DIM, generator=g) + n_sec = torch.randint(0, K_MAX, (batch_size,), generator=g) + sec_cont = torch.randn(batch_size, K_MAX, SEC_SLOT_DIM, generator=g) + proc_idx = torch.zeros(batch_size, dtype=torch.long) + batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx)) + return batches + + +def _model_config(cfg): + return { + "pdg_vocab": PDG_VOCAB, + "mat_vocab": MAT_VOCAB, + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], + } + + +def _run_train(cfg, out_dir, resume_path=None): + model_config = _model_config(cfg) + models = build_models(model_config) + critics = build_critics(model_config) + train_loader = _fake_batches(4, cfg["train"]["batch_size"]) + val_loader = _fake_batches(2, cfg["train"]["batch_size"], seed=1) + train( + cfg=cfg, + models=models, + critics=critics, + train_loader=train_loader, + val_loader=val_loader, + device=torch.device("cpu"), + out_dir=out_dir, + normalizer_dict={"cond": {}, "target": {}, "sec_phys": {}}, + pdg_map={"22": 0}, + mat_map={"G4_AIR": 0}, + proc_map=None, + model_config=model_config, + total_train_batches=4, + resume_path=resume_path, + ) + + +@pytest.mark.parametrize( + "label,mutate", + [ + ("both_flow", lambda cfg: None), + ("both_wgan", lambda cfg: cfg["stage1_model"].__setitem__("generator", "wgan")), + ( + "mixed_stage1_flow_stage2_wgan", + lambda cfg: None, # already the default + ), + ( + "mixed_stage1_wgan_stage2_flow", + lambda cfg: ( + cfg["stage1_model"].__setitem__("generator", "wgan"), + cfg["stage2_model"].__setitem__("generator", "flow"), + ), + ), + ("stage1_only", lambda cfg: cfg["stage2_model"].__setitem__("active", False)), + ("stage2_only", lambda cfg: cfg["stage1_model"].__setitem__("active", False)), + ( + "both_ddpm_stage1_flow_stage2", + lambda cfg: ( + cfg["stage1_model"].__setitem__("generator", "ddpm"), + cfg["stage2_model"].__setitem__("generator", "flow"), + ), + ), + ( + "routed_stage1_energy_gumbel", + lambda cfg: cfg["stage1_model"].__setitem__( + "router", + { + "enabled": True, + "type": "energy", + "n_experts": 3, + "temperature": 0.5, + "learn_centers": True, + "lambda_balance": 0.1, + "lambda_entropy": 0.01, + "gumbel": True, + "gumbel_tau_start": 1.0, + "gumbel_tau_end": 0.1, + }, + ), + ), + ], +) +def test_train_end_to_end(label, mutate): + cfg = _base_cfg() + mutate(cfg) + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + assert (out_dir / "last.pt").exists() + assert (out_dir / "metrics.csv").exists() + ckpt = torch.load(out_dir / "last.pt", weights_only=False) + if cfg["stage1_model"]["active"]: + assert "model" in ckpt + else: + assert "model" not in ckpt + if cfg["stage2_model"]["active"]: + assert "sec_decoder" in ckpt + else: + assert "sec_decoder" not in ckpt + + +def test_train_resume_continues_from_checkpoint(): + cfg = _base_cfg() + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + ckpt_before = torch.load(out_dir / "last.pt", weights_only=False) + assert ckpt_before["epoch"] == 2 + + cfg2 = copy.deepcopy(cfg) + cfg2["train"]["epochs"] = 3 + _run_train(cfg2, out_dir, resume_path=out_dir / "last.pt") + ckpt_after = torch.load(out_dir / "last.pt", weights_only=False) + assert ckpt_after["epoch"] == 3 + assert ckpt_after["global_step"] > ckpt_before["global_step"] + + +def test_train_raises_when_no_active_stage(): + cfg = _base_cfg() + cfg["stage1_model"]["active"] = False + cfg["stage2_model"]["active"] = False + model_config = _model_config(cfg) + models = build_models(model_config) + critics = build_critics(model_config) + with tempfile.TemporaryDirectory() as tmp: + with pytest.raises(ValueError, match="no active stage"): + train( + cfg=cfg, + models=models, + critics=critics, + train_loader=_fake_batches(1, 8), + val_loader=_fake_batches(1, 8), + device=torch.device("cpu"), + out_dir=Path(tmp) / "run", + model_config=model_config, + total_train_batches=1, + ) + + +def test_metrics_csv_columns_are_stage_prefixed(): + cfg = _base_cfg() + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + header = (out_dir / "metrics.csv").read_text().splitlines()[0].split(",") + assert "stage1_train_loss" in header + assert "stage2_train_d_loss" in header + assert "val_loss" in header + assert "epoch" in header + + +def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): + """Regression test: on a non-generator-step batch, if this stage's model + has no n_sec_head (n_sec defaults to stage 2, decision 1), g_loss is a + graph-less zero — .backward() must not be called on it.""" + cfg = _base_cfg() + cfg["stage1_model"]["generator"] = "wgan" + model_config = _model_config(cfg) + models = build_models(model_config) + critics = build_critics(model_config) + assert models["stage1"] is not None and critics["stage1"] is not None + trainer = WGANStageTrainer( + name="stage1", + model=models["stage1"], + critic=critics["stage1"], + is_stage2=False, + lambda_weight=1.0, + n_sec_lambda=0.1, + n_critic=1000, # never a generator step in this test + gp_weight=10.0, + lr=3e-4, + critic_lr=0.0, + ema_decay=0.0, + warmup_epochs=0, + epochs=1, + steps_per_epoch=4, + device=torch.device("cpu"), + ) + assert trainer.model.n_sec_head is None + batch = _fake_batches(1, 8)[0] + stats = trainer.step(batch, torch.device("cpu"), global_step=1) + assert stats["did_g_step"] is False + + +def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): + with pytest.raises(NotImplementedError): + FlowDDPMStageTrainer( + name="stage2", + model=torch.nn.Linear(1, 1), + is_stage2=True, + generator="ddpm", + lambda_weight=1.0, + n_sec_lambda=0.1, + lambda_balance=0.0, + lambda_proc=0.0, + lambda_entropy=0.0, + gumbel_tau_start=1.0, + gumbel_tau_end=0.1, + lr=3e-4, + weight_decay=0.01, + ema_decay=0.0, + warmup_epochs=0, + epochs=1, + steps_per_epoch=1, + ddpm_n_steps=50, + device=torch.device("cpu"), + ) -- 2.39.5 From 4fc15ecdfc6d0354dffe55fc0c62305d7a207b95 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Thu, 6 Aug 2026 15:43:48 +0200 Subject: [PATCH 07/30] v0.3.0 step 4: type map + particle_type.target = "onehot"/"embedding" Builds the shared top-N-plus-other PDG/material maps (pooling both primary and secondary occurrences for PDG, directly targeting the meeting's species-collapse failure mode) and wires up conditioning.{particle,material} = "onehot" plus stage2_model.particle_type.target in ("onehot", "embedding") end-to-end: setup-cache persistence, Stage2OneShot's type_head (flow/ddpm) vs. folded+ST-Gumbel-relaxed adversarial slice (wgan), and the corresponding CE/MSE training losses. particle_type.target = "physical" stays byte-for-byte unchanged, keeping the v0.2 migration shim's bit-identical guarantee intact. giant predict/rollout fail loudly on a onehot/embedding checkpoint until full decode support lands in step 6. Co-Authored-By: Claude Sonnet 5 --- giant/cli.py | 39 +++++- giant/data/dataset.py | 36 +++++- giant/data/loader.py | 116 ++++++++++++++++-- giant/data/setup_cache.py | 45 ++++++- giant/data/transforms.py | 149 ++++++++++++++++++----- giant/model/network.py | 147 +++++++++++++++++++++-- giant/model/schedule.py | 40 +++++-- giant/pipeline.py | 114 ++++++++++++++++-- giant/train.py | 245 +++++++++++++++++++++++++++++++++++--- tests/test_cli_predict.py | 58 +++++++++ tests/test_loader.py | 60 ++++++++++ tests/test_network.py | 223 +++++++++++++++++++++++++++++++++- tests/test_pipeline.py | 33 +++++ tests/test_setup_cache.py | 27 +++++ tests/test_train.py | 41 ++++++- tests/test_transforms.py | 6 +- 16 files changed, 1277 insertions(+), 102 deletions(-) diff --git a/giant/cli.py b/giant/cli.py index 1abf63b..99dfdba 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -86,6 +86,41 @@ def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: return raw +def _check_v030_onehot_support(model_cfg: dict, command: str) -> None: + """`giant predict`/`giant rollout` don't yet support conditioning + `"onehot"` mode or `stage2_model.particle_type.target` in `("onehot", + "embedding")` — full decode (class index -> concrete PDG, `other_policy` + sampling) is v0.3.0 step 6 (docs/v0.3.0-design.md), which also lands the + autoregressive decoder these targets are meant to pair with. Without + this guard, predict/rollout would crash later on a `cond_cat`/`sec_dim` + shape mismatch (onehot) or silently read a meaningless raw (log_mass, + charge) pair (embedding) instead of failing clearly. A v0.2 (flat) + `model_config` never has these, so this is a no-op there. + """ + conditioning = model_cfg.get("conditioning") + if not isinstance(conditioning, dict): + return + particle_type = conditioning.get("particle", {}).get("type") + material_type = conditioning.get("material", {}).get("type") + particle_type_target = ( + model_cfg.get("stage2_model", {}).get("particle_type", {}).get("target") + ) + if ( + particle_type == "onehot" + or material_type == "onehot" + or particle_type_target in ("onehot", "embedding") + ): + typer.echo( + f"error: giant {command} does not yet support conditioning " + "onehot mode or stage2_model.particle_type.target in " + "('onehot', 'embedding') — full decode (class index -> concrete " + "PDG) lands in v0.3.0 step 6 alongside the autoregressive " + "decoder these targets are meant to pair with.", + err=True, + ) + raise typer.Exit(1) + + def _batch_size_estimate_dims( model_cfg: dict, training: bool, stage: str = "stage1" ) -> tuple[int, int]: @@ -930,6 +965,7 @@ def predict( raise typer.Exit(1) model_cfg = ckpt["model_config"] + _check_v030_onehot_support(model_cfg, "predict") if batch_size_auto: est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( @@ -999,7 +1035,7 @@ def predict( nonlocal writer, total if coord == Coord.local: - cond_cont, cond_cat, target_raw, _, _, _, _, _ = build_features( + cond_cont, cond_cat, target_raw, _, _, _, _, _, _ = build_features( piece, pdg_map, mat_map, conditioning=conditioning ) cond_cont = cond_norm.transform(cond_cont) @@ -1342,6 +1378,7 @@ def rollout( training_cfg = gconfig.load_checkpoint_config(checkpoint) model_cfg = ckpt["model_config"] + _check_v030_onehot_support(model_cfg, "rollout") conditioning = _conditioning_str(model_cfg) pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} diff --git a/giant/data/dataset.py b/giant/data/dataset.py index 3b56a79..3736b92 100644 --- a/giant/data/dataset.py +++ b/giant/data/dataset.py @@ -39,17 +39,24 @@ class StreamingStepsDataset(IterableDataset): numpy slicing instead of a per-row Python loop in the default collate. Each batch is a tuple: - (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx) + (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, sec_type_idx) where: cond_cont: (B, COND_DIM) float32 - cond_cat: (B, 2) int64 + cond_cat: (B, 2/3/4) int64 — width 2 unless conditioning="onehot" + (see docs/v0.3.0-design.md decision 4) target_s1: (B, 9) float32 — normalised Stage-1 primary target n_sec: (B,) int64 — true secondary count per step sec_cont: (B, K_MAX, SEC_SLOT_DIM) float32 — [stick_logit, local_dir, log_mass, charge] per slot (mass/charge - normalised iff `sec_phys_normalizer` was given) + normalised iff `sec_phys_normalizer` was given); always + computed the same way regardless of + stage2_model.particle_type.target, only actually used + downstream under target="physical" proc_idx: (B,) int64 — process-class label (ProcessRouter supervision only; zeros when `proc_map` is None) + sec_type_idx: (B, K_MAX) int64 — per-slot class index into + `sec_type_class_map`, for particle_type.target in + ("onehot", "embedding"); zeros (unused) otherwise """ def __init__( @@ -66,6 +73,9 @@ class StreamingStepsDataset(IterableDataset): proc_map: dict[str, int] | None = None, conditioning: str = "embedding", sec_phys_normalizer: Normalizer | None = None, + pdg_topn_map: dict[int, int] | None = None, + mat_topn_map: dict[str, int] | None = None, + sec_type_class_map: dict | None = None, ) -> None: self.files = list(files) self._offsets = {path: event_id_offset(i) for i, path in enumerate(self.files)} @@ -81,6 +91,9 @@ class StreamingStepsDataset(IterableDataset): self.proc_map = proc_map self.conditioning = conditioning self.sec_phys_normalizer = sec_phys_normalizer + self.pdg_topn_map = pdg_topn_map + self.mat_topn_map = mat_topn_map + self.sec_type_class_map = sec_type_class_map def __iter__(self): worker_info = torch.utils.data.get_worker_info() @@ -98,6 +111,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec: list[np.ndarray] = [] buf_sec: list[np.ndarray] = [] buf_proc: list[np.ndarray] = [] + buf_type: list[np.ndarray] = [] buf_n = 0 for path in files: @@ -114,6 +128,7 @@ class StreamingStepsDataset(IterableDataset): n_sec, sec_cont, proc_idx, + sec_type_idx, _, _, ) = build_features( @@ -126,6 +141,9 @@ class StreamingStepsDataset(IterableDataset): proc_map=self.proc_map, require_secondaries=True, conditioning=self.conditioning, + pdg_topn_map=self.pdg_topn_map, + mat_topn_map=self.mat_topn_map, + sec_type_class_map=self.sec_type_class_map, ) buf_cont.append(cond_cont) buf_cat.append(cond_cat) @@ -133,6 +151,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec.append(n_sec) buf_sec.append(sec_cont) buf_proc.append(proc_idx) + buf_type.append(sec_type_idx) buf_n += len(cond_cont) if buf_n >= self.shuffle_buffer: @@ -143,6 +162,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec, buf_sec, buf_proc, + buf_type, buf_n, ) = yield from self._flush( buf_cont, @@ -151,6 +171,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec, buf_sec, buf_proc, + buf_type, final=False, ) @@ -162,6 +183,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec, buf_sec, buf_proc, + buf_type, final=True, ) @@ -173,6 +195,7 @@ class StreamingStepsDataset(IterableDataset): buf_nsec: list[np.ndarray], buf_sec: list[np.ndarray], buf_proc: list[np.ndarray], + buf_type: list[np.ndarray], final: bool, ): cont = np.concatenate(buf_cont) @@ -181,11 +204,12 @@ class StreamingStepsDataset(IterableDataset): nsec = np.concatenate(buf_nsec) sec = np.concatenate(buf_sec) proc = np.concatenate(buf_proc) + styp = np.concatenate(buf_type) if self.shuffle: idx = np.random.permutation(len(cont)) cont, cat, tgt = cont[idx], cat[idx], tgt[idx] - nsec, sec, proc = nsec[idx], sec[idx], proc[idx] + nsec, sec, proc, styp = nsec[idx], sec[idx], proc[idx], styp[idx] bs = self.batch_size n = len(cont) @@ -199,10 +223,11 @@ class StreamingStepsDataset(IterableDataset): torch.from_numpy(nsec[start:end]).long(), torch.from_numpy(sec[start:end]).float(), torch.from_numpy(proc[start:end]).long(), + torch.from_numpy(styp[start:end]).long(), ) if final: - return [], [], [], [], [], [], 0 + return [], [], [], [], [], [], [], 0 rem = n_full * bs return ( [cont[rem:]], @@ -211,5 +236,6 @@ class StreamingStepsDataset(IterableDataset): [nsec[rem:]], [sec[rem:]], [proc[rem:]], + [styp[rem:]], n - rem, ) diff --git a/giant/data/loader.py b/giant/data/loader.py index e76af49..40f6a6b 100644 --- a/giant/data/loader.py +++ b/giant/data/loader.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass from pathlib import Path from typing import Iterator @@ -243,6 +244,45 @@ def build_index_maps_from_files( ) +def _accumulate_value_counts(counts: dict, series: pd.Series, cast) -> None: + for name, count in series.value_counts().items(): + name = cast(name) + counts[name] = counts.get(name, 0) + int(count) + + +def _rank_by_frequency_from_files(files: list[Path], column: str, cast) -> dict: + """Scan `column` across `files` and return `{cast(value): total_count}`, + accumulated in file order (see `fingerprint_files`'s docstring on why + scan order — not a normalized/sorted order — is preserved: it drives + tie-breaking in the frequency ranking below).""" + counts: dict = {} + for path in files: + df = pd.read_parquet(path, columns=[column]) + _accumulate_value_counts(counts, df[column], cast) + return counts + + +def _topn_plus_other_map(counts: dict, n_classes: int) -> tuple[dict, dict]: + """Frequency-capped value->index map: the `n_classes - 1` most frequent + keys get their own index; every rarer key is bucketed into a shared + "other" index (`n_classes - 1`). + + Returns `(class_map, other_members)` — `other_members` is `{key: count}` + for every key bucketed into "other" (the empirical within-bucket + distribution, for `other_policy = "sample"` at rollout — see + docs/v0.3.0-design.md §8). + """ + ranked = sorted(counts, key=lambda k: counts[k], reverse=True) + keep = ranked[: max(n_classes - 1, 0)] + class_map = {k: i for i, k in enumerate(keep)} + other_idx = n_classes - 1 + other_members: dict = {} + for k in ranked[len(keep) :]: + class_map[k] = other_idx + other_members[k] = counts[k] + return class_map, other_members + + def build_process_map_from_files(files: list[Path], n_experts: int) -> dict[str, int]: """Scan the `process` column and build a frequency-capped process->index map. @@ -253,16 +293,68 @@ def build_process_map_from_files(files: list[Path], n_experts: int) -> dict[str, mirrors how `build_features` clamps the n_sec label to K_MAX for the fixed-width n_sec_head classifier. """ - counts: dict[str, int] = {} + counts = _rank_by_frequency_from_files(files, "process", str) + class_map, _ = _topn_plus_other_map(counts, n_experts) + return class_map + + +@dataclass +class TopNMap: + """A frequency-capped value->index map for a conditioning/type axis (PDG + or material), plus the empirical within-bucket distribution of whatever + got folded into "other" — see `build_topn_map_from_files`.""" + + class_map: dict + other_members: dict + + +def build_topn_map_from_files( + files: list[Path], column: str, n_classes: int, cast=str +) -> TopNMap: + """Scan `column` and build a frequency-capped value->index map, structurally + identical to `build_process_map_from_files` (shares its ranking core via + `_topn_plus_other_map`), generalized over the source column and key type. + + Used for the material axis (`column="material"`, `cast=str`, matching + `mat_map`'s key type) — see docs/v0.3.0-design.md §8. The PDG axis uses + `build_pdg_topn_map_from_files` instead (it needs to pool two columns, + which this single-column form can't express). Also records + `other_members` (the empirical within-"other" distribution), needed + later for `other_policy = "sample"` at rollout — computed now since it's + free during this same scan. + """ + counts = _rank_by_frequency_from_files(files, column, cast) + class_map, other_members = _topn_plus_other_map(counts, n_classes) + return TopNMap(class_map=class_map, other_members=other_members) + + +def build_pdg_topn_map_from_files(files: list[Path], n_classes: int) -> TopNMap: + """PDG top-N-plus-other map, pooling counts from BOTH roles a PDG code + plays in this dataset: a step's own primary particle (`pdg` column) and + an emitted secondary's species (`sec_pdg_list`, exploded) — shared by + `conditioning.particle.type = "onehot"` and + `stage2_model.particle_type.target = "onehot"` (docs/v0.3.0-design.md + §8). Pooling both is what keeps a species that's common as a secondary + but rare as a primary (or vice versa) from being pushed into "other" + just because one role's count alone looks small — the meeting's failure + mode (zero photon secondaries, hallucinated antineutrinos) was + specifically about secondary-species collapse, so the map this feeds + needs to reflect secondary frequency, not just primary frequency. + + `sec_pdg_list` is absent from parquet files predating the parent->child + join (see `_df_to_dict`'s `has_sec_lists` guard) — silently skipped for + those, same convention as elsewhere in this module. + """ + counts: dict = {} for path in files: - df = pd.read_parquet(path, columns=["process"]) - for name, count in df["process"].value_counts().items(): - name = str(name) - counts[name] = counts.get(name, 0) + int(count) - ranked = sorted(counts, key=lambda name: counts[name], reverse=True) - keep = ranked[: max(n_experts - 1, 0)] - proc_map = {name: i for i, name in enumerate(keep)} - other_idx = n_experts - 1 - for name in ranked[len(keep) :]: - proc_map[name] = other_idx - return proc_map + columns = ["pdg"] + has_sec = "sec_pdg_list" in pq.ParquetFile(path).schema_arrow.names + if has_sec: + columns.append("sec_pdg_list") + df = pd.read_parquet(path, columns=columns) + _accumulate_value_counts(counts, df["pdg"], int) + if has_sec: + exploded = df["sec_pdg_list"].explode().dropna() + _accumulate_value_counts(counts, exploded, int) + class_map, other_members = _topn_plus_other_map(counts, n_classes) + return TopNMap(class_map=class_map, other_members=other_members) diff --git a/giant/data/setup_cache.py b/giant/data/setup_cache.py index b1c69d4..a20d0b6 100644 --- a/giant/data/setup_cache.py +++ b/giant/data/setup_cache.py @@ -23,7 +23,7 @@ import numpy as np from giant import config from giant.constants import COND_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_SLOT_DIM, X_DIM -from giant.data.loader import event_id_offset, load_event_ids +from giant.data.loader import TopNMap, event_id_offset, load_event_ids from giant.data.transforms import Normalizer, sorted_membership # Bump manually on a change to the data-encoding semantics (e.g. a future @@ -102,6 +102,40 @@ def normalizer_key(val_fraction: float, seed: int, conditioning: str) -> str: return f"valfrac={val_fraction:.6g}_seed={seed}_cond={conditioning}" +# Top-N-map axes (docs/v0.3.0-design.md §8): "pdg" keys match pdg_map's int +# keys (shared by conditioning.particle.type="onehot" and +# stage2_model.particle_type.target="onehot" — one map for both), "material" +# keys match mat_map's str keys. +_TOPN_AXIS_CASTS = {"pdg": int, "material": str} + + +def topn_key(axis: str, n_classes: int) -> str: + """JSON-safe key for `SetupCache.topn_maps` — N is part of the key so the + sidecar stays reusable across runs with different emb_dim (see the + dict[int, dict] precedent `proc_maps` sets, keyed by n_experts).""" + if axis not in _TOPN_AXIS_CASTS: + raise ValueError( + f"unknown top-N map axis {axis!r}, expected one of " + f"{sorted(_TOPN_AXIS_CASTS)}" + ) + return f"{axis}:{n_classes}" + + +def topnmap_to_json(m: TopNMap) -> dict: + return { + "class_map": {str(k): v for k, v in m.class_map.items()}, + "other_members": {str(k): v for k, v in m.other_members.items()}, + } + + +def topnmap_from_json(d: dict, axis: str) -> TopNMap: + cast = _TOPN_AXIS_CASTS[axis] + return TopNMap( + class_map={cast(k): v for k, v in d["class_map"].items()}, + other_members={cast(k): v for k, v in d["other_members"].items()}, + ) + + @dataclass class NormalizerEntry: cond_norm: Normalizer @@ -143,6 +177,8 @@ class SetupCache: event_index: tuple[np.ndarray, np.ndarray] | None = None proc_maps: dict[int, dict[str, int]] = field(default_factory=dict) normalizers: dict[str, NormalizerEntry] = field(default_factory=dict) + topn_maps: dict[str, TopNMap] = field(default_factory=dict) + """Keyed by `topn_key(axis, n_classes)` — see docs/v0.3.0-design.md §8.""" @classmethod def empty(cls, files: list[Path]) -> "SetupCache": @@ -156,6 +192,7 @@ class SetupCache: "fingerprint": self.fingerprint, "proc_maps": {str(k): v for k, v in self.proc_maps.items()}, "normalizers": {k: v.to_json() for k, v in self.normalizers.items()}, + "topn_maps": {k: topnmap_to_json(v) for k, v in self.topn_maps.items()}, } if self.vocab is not None: pdg_map, mat_map = self.vocab @@ -188,6 +225,10 @@ class SetupCache: normalizers = { k: NormalizerEntry.from_json(v) for k, v in d.get("normalizers", {}).items() } + topn_maps = { + k: topnmap_from_json(v, axis=k.split(":", 1)[0]) + for k, v in d.get("topn_maps", {}).items() + } return cls( fingerprint=d["fingerprint"], git_hash=d.get("git_hash", "unknown"), @@ -195,6 +236,7 @@ class SetupCache: event_index=event_index, proc_maps=proc_maps, normalizers=normalizers, + topn_maps=topn_maps, ) def merge(self, other: "SetupCache") -> "SetupCache": @@ -214,6 +256,7 @@ class SetupCache: ), proc_maps={**self.proc_maps, **other.proc_maps}, normalizers={**self.normalizers, **other.normalizers}, + topn_maps={**self.topn_maps, **other.topn_maps}, ) diff --git a/giant/data/transforms.py b/giant/data/transforms.py index e41041e..a605cab 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -340,21 +340,22 @@ def sorted_membership(values: np.ndarray, sorted_arr: np.ndarray) -> np.ndarray: def _vectorized_map_lookup( - values: np.ndarray, mapping: dict, strict: bool = True + values: np.ndarray, mapping: dict, strict: bool = True, default: int = 0 ) -> np.ndarray: """Vectorized equivalent of `np.array([mapping[v] for v in values], dtype=np.int64)`. Replaces a per-element Python dict lookup with one `searchsorted` call. Raises `KeyError` if any value in `values` isn't a key of `mapping`, matching the dict-comprehension it replaces (never silently misassigns) - — unless `strict=False`, in which case unmapped values get a dummy index - of 0 instead. Only pass `strict=False` where the caller has independently - verified the resulting index is never actually read (e.g. + — unless `strict=False`, in which case unmapped values get `default` + instead. Only pass `strict=False` where the caller has independently + verified the resulting index is either never actually read (e.g. `build_cond_features` under `conditioning="physical"`, where - `ConditionEncoder` ignores `cond_cat` entirely); it exists so a rollout - can be seeded with a species/material outside the training vocab without - a spurious `KeyError`, which is the entire point of physical-property - conditioning. + `ConditionEncoder` ignores `cond_cat` entirely) or where `default` is a + deliberate fallback class (e.g. a top-N map's "other" index for a raw + value outside the training vocab). It exists so a rollout can be seeded + with a species/material outside the training vocab without a spurious + `KeyError`, which is the entire point of physical-property conditioning. """ keys = np.asarray(list(mapping.keys())) vals = np.asarray(list(mapping.values()), dtype=np.int64) @@ -366,7 +367,7 @@ def _vectorized_map_lookup( found = keys_sorted[pos] == values if not found.all(): if not strict: - out = np.zeros(values.shape, dtype=np.int64) + out = np.full(values.shape, default, dtype=np.int64) out[found] = vals_sorted[pos[found]] return out missing = np.unique(values[~found]) @@ -550,6 +551,42 @@ def encode_secondaries( return sec_cont.astype(np.float32) +def encode_secondary_type_idx( + sec_pdg_list: np.ndarray, sec_valid: np.ndarray, class_map: dict +) -> np.ndarray: + """Per-secondary-slot class index into `class_map` — (N, K_MAX) int64. + + `class_map` is either a top-N-plus-other map's `class_map` + (`stage2_model.particle_type.target = "onehot"`, see + `giant.data.loader.build_pdg_topn_map_from_files`) or the dense `pdg_map` + (`target = "embedding"`). Not used at all for `target = "physical"` + (see docs/v0.3.0-design.md decision 1) — that target keeps using + `encode_secondaries`'s (log_mass, charge) columns unchanged. + + Padding slots get index 0 (their looked-up value is discarded downstream + by the `sec_valid`/`n_sec` mask regardless, so any in-vocabulary dummy + code works). A *real, valid* secondary whose code is missing from + `class_map` raises `KeyError` (`strict=True`) rather than silently + misassigning — for `target="onehot"` this should never actually + trigger, since `build_pdg_topn_map_from_files` pools both primary and + secondary occurrences precisely so every secondary species seen in + these files has a key (in "other" at worst); for `target="embedding"` + (which reuses the dense, primary-only `pdg_map`) it's a real signal + that a secondary-only species exists with no primary-role counterpart. + """ + N, K = sec_pdg_list.shape + # An arbitrary already-present key works as the padding-slot dummy code + # (unlike encode_secondaries' physics-derived phys lookup, this is an + # index into class_map's own vocabulary, so a fixed sentinel like 22 + # isn't guaranteed to be a key — an arbitrary present one always is). + dummy = next(iter(class_map)) + safe_pdg = np.where(sec_valid, sec_pdg_list, dummy) + idx = _vectorized_map_lookup(safe_pdg.reshape(-1), class_map, strict=True).reshape( + N, K + ) + return np.where(sec_valid, idx, 0).astype(np.int64) + + def decode_secondaries( sec_cont: np.ndarray, n_sec: np.ndarray, @@ -643,19 +680,20 @@ def _physical_cond_columns( ) -> np.ndarray: """(N, PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM) physical conditioning columns. - "embedding" mode zero-fills (cheap, and ConditionEncoder never reads - these columns in that mode — so an unfilled giant.materials table can - never crash an "embedding"-mode run). "physical" mode computes them for - real: particle columns come from `data["mass"]`/`data["charge"]` when the - caller already knows them directly (rollout.py, for a track descended - from a model-predicted secondary — see giant/rollout.py's "no snapping" - design), else derived from `data["pdg"]` via giant.particles; material - columns always come from `data["material"]` via giant.materials, since - material is never itself a model prediction. + "embedding"/"onehot" modes zero-fill (cheap, and ConditionEncoder never + reads these columns in either mode — so an unfilled giant.materials table + can never crash an "embedding"/"onehot"-mode run). "physical" mode + computes them for real: particle columns come from + `data["mass"]`/`data["charge"]` when the caller already knows them + directly (rollout.py, for a track descended from a model-predicted + secondary — see giant/rollout.py's "no snapping" design), else derived + from `data["pdg"]` via giant.particles; material columns always come + from `data["material"]` via giant.materials, since material is never + itself a model prediction. """ from giant.constants import MATERIAL_PHYS_DIM, PARTICLE_PHYS_DIM - if conditioning == "embedding": + if conditioning in ("embedding", "onehot"): n = len(next(iter(data.values()))) return np.zeros((n, PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM), dtype=np.float32) if conditioning != "physical": @@ -693,8 +731,19 @@ def build_cond_features( mat_map: dict[str, int], cond_normalizer: "Normalizer | None" = None, conditioning: str = "embedding", + pdg_topn_map: dict[int, int] | None = None, + mat_topn_map: dict[str, int] | None = None, ) -> tuple[np.ndarray, np.ndarray]: - """Build conditioning arrays only — no target, no post-step variables.""" + """Build conditioning arrays only — no target, no post-step variables. + + `pdg_topn_map`/`mat_topn_map` (a top-N-plus-other `class_map`, see + `giant.data.loader.build_topn_map_from_files`) append extra `cond_cat` + columns read by `ConditionEncoder`'s `"onehot"` mode + (docs/v0.3.0-design.md decision 4): pdg topN index at column 2 (iff + `pdg_topn_map` given), material topN index at column 3 (iff + `mat_topn_map` given, after column 2 if both are). Only ever given when + `conditioning == "onehot"`; `cond_cat` stays `(N, 2)` otherwise. + """ cond_cont = np.column_stack( [ data["pre_pos"], @@ -707,16 +756,24 @@ def build_cond_features( [cond_cont, _physical_cond_columns(data, conditioning)] ).astype(np.float32) - # In "physical" mode cond_cat is only a reporting/router convenience — - # ConditionEncoder never reads it (giant/model/network.py) — so a - # species/material outside the training vocab (the whole point of - # physical-property conditioning) gets a dummy index instead of raising. - # In "embedding" mode cond_cat IS the conditioning signal, so an unmapped - # value must still raise loudly rather than silently misassign. + # In "physical" mode cond_cat's first two columns are only a + # reporting/router convenience — ConditionEncoder never reads them + # (giant/model/network.py) — so a species/material outside the training + # vocab (the whole point of physical-property conditioning) gets a dummy + # index instead of raising. In "embedding" mode those columns ARE the + # conditioning signal, so an unmapped value must still raise loudly + # rather than silently misassign. In "onehot" mode they again go unread + # (the topN columns below are the real signal), so they're as permissive + # as "physical". strict = conditioning == "embedding" pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map, strict=strict) mat_idx = _vectorized_map_lookup(data["material"], mat_map, strict=strict) - cond_cat = np.column_stack([pdg_idx, mat_idx]) + cat_cols = [pdg_idx, mat_idx] + if pdg_topn_map is not None: + cat_cols.append(_vectorized_map_lookup(data["pdg"], pdg_topn_map)) + if mat_topn_map is not None: + cat_cols.append(_vectorized_map_lookup(data["material"], mat_topn_map)) + cond_cat = np.column_stack(cat_cols) if cond_normalizer is not None: cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning) @@ -769,6 +826,9 @@ def build_features( require_secondaries: bool = False, conditioning: str = "embedding", sec_phys_only: bool = False, + pdg_topn_map: dict[int, int] | None = None, + mat_topn_map: dict[str, int] | None = None, + sec_type_class_map: dict | None = None, ) -> tuple[ np.ndarray, np.ndarray, @@ -776,10 +836,12 @@ def build_features( np.ndarray, np.ndarray, np.ndarray, + np.ndarray, Normalizer | None, Normalizer | None, ]: - """Assemble (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx) arrays. + """Assemble (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, + sec_type_idx) arrays. target_s1: (N, 9) Stage-1 primary post-step target (unchanged from Phase 1) n_sec: (N,) integer secondary counts (target for n_sec head) @@ -787,9 +849,18 @@ def build_features( [stick_logit, dir_local, log_mass, charge] — mass/charge are the secondary's real physical identity (from its ground-truth PDG code), a fixed regression target, not a learned/snapped one. + Always computed the same way regardless of + `stage2_model.particle_type.target` (docs/v0.3.0-design.md + decision 1/3) — only actually used downstream under `target = + "physical"`. proc_idx: (N,) integer process-class label (ProcessRouter supervision only — never conditioning). Zeros when `proc_map` is None or the loaded data has no "process" column (e.g. pre-conversion parquet files). + sec_type_idx: (N, K_MAX) integer secondary class index into + `sec_type_class_map`, for `stage2_model.particle_type.target` + in `("onehot", "embedding")` — see `encode_secondary_type_idx`. + Zero-filled (and unused) when `sec_type_class_map` is None + (i.e. `target = "physical"`). require_secondaries: when True, raise if any step has n_sec > 0 but the per-secondary list columns are absent (a mis-converted file that would @@ -800,6 +871,14 @@ def build_features( the stick-breaking/direction-rotation blocks of `sec_cont` (zero-filled instead) for callers (normalizer fitting) that only read `sec_cont[:, :, 4:6]` and would otherwise discard that work. + + pdg_topn_map/mat_topn_map: appended `cond_cat` columns for + `ConditionEncoder`'s `"onehot"` mode — see `build_cond_features`. + + sec_type_class_map: the map `sec_type_idx` is looked up against — a + top-N-plus-other map's `class_map` for `target = "onehot"`, or the + dense `pdg_map` for `target = "embedding"` (pass `pdg_map` itself). + `None` for `target = "physical"`. """ from giant.constants import K_MAX @@ -836,7 +915,12 @@ def build_features( pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map) mat_idx = _vectorized_map_lookup(data["material"], mat_map) - cond_cat = np.column_stack([pdg_idx, mat_idx]) # (N, 2) + cat_cols = [pdg_idx, mat_idx] + if pdg_topn_map is not None: + cat_cols.append(_vectorized_map_lookup(data["pdg"], pdg_topn_map)) + if mat_topn_map is not None: + cat_cols.append(_vectorized_map_lookup(data["material"], mat_topn_map)) + cond_cat = np.column_stack(cat_cols) # (N, 2/3/4) — see decision 4 n_sec_raw = data["n_sec"].astype( np.int64 @@ -864,6 +948,11 @@ def build_features( sec_pdg_list=sec_pdg_list, phys_only=sec_phys_only, ) # (N, K_MAX, 6) + sec_type_idx = ( + encode_secondary_type_idx(sec_pdg_list, sec_valid, sec_type_class_map) + if sec_type_class_map is not None + else np.zeros((len(n_sec), K_MAX), dtype=np.int64) + ) else: # Guard against silently training Stage 2 on zeroed targets: if any step # actually spawned secondaries (n_sec > 0, from child_track_ids) but the @@ -886,6 +975,7 @@ def build_features( ) N = len(n_sec) sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32) + sec_type_idx = np.zeros((N, K_MAX), dtype=np.int64) if fit: cond_normalizer = Normalizer().fit(cond_cont) @@ -914,6 +1004,7 @@ def build_features( n_sec, sec_cont, proc_idx, + sec_type_idx, cond_normalizer, target_normalizer, ) diff --git a/giant/model/network.py b/giant/model/network.py index 88e89e9..3a52b32 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -10,6 +10,7 @@ import torch.nn.functional as F from giant.constants import ( COND_DIM, COND_DIM_BASE, + CONT_SLOT_DIM, EMB_DIM, K_MAX, MATERIAL_PHYS_DIM, @@ -42,6 +43,30 @@ class SinusoidalEmbedding(nn.Module): return torch.cat([args.sin(), args.cos()], dim=-1) # (B, dim) +def cat_col_layout( + particle_type: str, material_type: str +) -> tuple[int | None, int | None]: + """`cond_cat` column indices for each axis's top-N-onehot index, or + `None` if that axis isn't `"onehot"` (docs/v0.3.0-design.md decision 4). + + Columns 0/1 are always the dense pdg/material vocab index. The particle + top-N column (if any) comes next, then the material top-N column (if + any) — `giant.data.transforms.build_cond_features`/`build_features` + append columns in this same order, so the two sides must never drift + apart. + """ + col = 2 + particle_col = None + if particle_type == "onehot": + particle_col = col + col += 1 + material_col = None + if material_type == "onehot": + material_col = col + col += 1 + return particle_col, material_col + + def _make_axis_mlp(in_dim: int, emb_dim: int, n_layers: int) -> nn.Sequential: """`n_layers`-deep MLP producing an `emb_dim`-wide vector from `in_dim` physical properties (`conditioning.{particle,material}.n_layers`). @@ -76,8 +101,11 @@ class ConditionEncoder(nn.Module): properties (already present in `cond_cont[:, COND_DIM_BASE:]` — see giant.data.transforms.build_features), computable for any PDG code / material name rather than only ones seen in training. - - "onehot": not yet implemented (v0.3.0 step 4 — the top-N map isn't - built yet); raises `NotImplementedError` if selected. + - "onehot": a fixed, unlearned one-hot vector over a top-N-plus-other + class map (`giant.data.loader.build_topn_map_from_files`/ + `build_pdg_topn_map_from_files`), read from `cond_cat`'s extra + top-N-index column(s) — see `_cat_col_layout` and + docs/v0.3.0-design.md decision 4. """ def __init__( @@ -92,6 +120,9 @@ class ConditionEncoder(nn.Module): super().__init__() self.particle_cfg = dict(particle_cfg) self.material_cfg = dict(material_cfg) + self._particle_topn_col, self._material_topn_col = cat_col_layout( + particle_cfg["type"], material_cfg["type"] + ) p_type = particle_cfg["type"] p_emb_dim = particle_cfg["emb_dim"] @@ -131,10 +162,11 @@ class ConditionEncoder(nn.Module): :, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM ] return self.particle_mlp(particle_phys) - raise NotImplementedError( - "conditioning.particle.type='onehot' needs the top-N PDG map " - "(v0.3.0 step 4, not yet implemented)" - ) + assert self._particle_topn_col is not None + return F.one_hot( + cond_cat[:, self._particle_topn_col], + num_classes=self.particle_cfg["emb_dim"], + ).float() def _material_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor): m_type = self.material_cfg["type"] @@ -143,10 +175,11 @@ class ConditionEncoder(nn.Module): if m_type == "physical": material_phys = cond_cont[:, COND_DIM_BASE + PARTICLE_PHYS_DIM :] return self.material_mlp(material_phys) - raise NotImplementedError( - "conditioning.material.type='onehot' needs the top-N material " - "map (v0.3.0 step 4, not yet implemented)" - ) + assert self._material_topn_col is not None + return F.one_hot( + cond_cat[:, self._material_topn_col], + num_classes=self.material_cfg["emb_dim"], + ).float() def forward(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: pdg_e = self._particle_embed(cond_cont, cond_cat) @@ -754,6 +787,40 @@ def build_trunk( # --------------------------------------------------------------------------- +def stage2_type_dim(particle_type_cfg: dict, emb_dim: int) -> int: + """Width of a single secondary slot's type slice — + `PARTICLE_PHYS_DIM` (log_mass, charge) for `target = "physical"`, else + `emb_dim` (both `"onehot"` class logits and `"embedding"` vectors are + `conditioning.particle.emb_dim` wide — docs/v0.3.0-design.md §3.3).""" + target = particle_type_cfg.get("target", "physical") + return PARTICLE_PHYS_DIM if target == "physical" else emb_dim + + +def stage2_trunk_sec_dim( + particle_type_cfg: dict, generator: str, k_max: int, emb_dim: int +) -> int: + """`Stage2OneShot`'s trunk output width (docs/v0.3.0-design.md decision 2). + + `target = "physical"` is untouched from v0.2/today (decision 1): + `k_max * SEC_SLOT_DIM`, the type slice folded into the same + flow-matched/WGAN vector as the continuous stick/dir slots. + + `target` in `("onehot", "embedding")`: under `generator == "wgan"` the + type slice is still folded in (adversarial for onehot via ST-Gumbel, + already-continuous for embedding — §2.1), just `emb_dim` wide instead of + `PARTICLE_PHYS_DIM` wide: `k_max * (CONT_SLOT_DIM + emb_dim)`. Under + `generator in ("flow", "ddpm")` the type slice isn't part of this vector + at all — it's `Stage2OneShot.type_head`'s job instead — so the trunk + only covers `k_max * CONT_SLOT_DIM`. + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return k_max * SEC_SLOT_DIM + if generator == "wgan": + return k_max * (CONT_SLOT_DIM + emb_dim) + return k_max * CONT_SLOT_DIM + + class Stage1Model(nn.Module): """Predicts the 9D primary post-step vector. No `n_sec_head` — decision 1 (docs/v0.3.0-design.md §2) moves it to stage 2, except for a migrated @@ -839,6 +906,19 @@ class Stage2OneShot(nn.Module): Owns `n_sec_head` by default (decision 1) unless `build_n_sec_head=False` (a migrated v0.2 checkpoint, whose n_sec_head instead attaches to Stage1Model — see `_migrate_legacy_model_config`). + + `particle_type_cfg["target"]` (default `"physical"`) selects the + secondary-type mechanism (docs/v0.3.0-design.md decision 2): + `"physical"` keeps the type slice folded into the trunk's own + flow-matched/WGAN output, unchanged from v0.2 (`sec_dim` — computed by + the caller via `stage2_trunk_sec_dim` — already reflects this). Under + `"onehot"`/`"embedding"` with `generator in ("flow", "ddpm")`, the type + slice is predicted by a separate `type_head` instead (same shape pattern + as `n_sec_head`) — `sec_dim` then covers only the continuous + stick/dir slots, `type_head` covers `k_max * emb_dim` type logits/vectors. + Under `generator == "wgan"` the type slice stays folded into `sec_dim` + (just `emb_dim` instead of `PARTICLE_PHYS_DIM` wide) and `type_head` is + unused (`None`) — the WGAN trainer handles the ST-Gumbel relaxation. """ def __init__( @@ -860,10 +940,12 @@ class Stage2OneShot(nn.Module): k_max: int = K_MAX, router: Router | None = None, build_n_sec_head: bool = True, + particle_type_cfg: dict | None = None, ) -> None: super().__init__() self.generator_kind = generator self.noise_dim = noise_dim + self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) self.cond_enc = ConditionEncoder( pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim ) @@ -886,6 +968,17 @@ class Stage2OneShot(nn.Module): nn.SiLU(), nn.Linear(hidden_dim // 2, k_max + 1), ) + self.type_head = None + target = self.particle_type_cfg.get("target", "physical") + if target != "physical" and generator in ("flow", "ddpm"): + emb_dim = particle_cfg["emb_dim"] + self.type_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, k_max * emb_dim), + ) + self._type_k_max = k_max + self._type_emb_dim = emb_dim def _cond_embed( self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor @@ -925,6 +1018,27 @@ class Stage2OneShot(nn.Module): c_emb = self._cond_embed(cond_cont, cond_cat, stage1_out) return self.n_sec_head(c_emb) + def predict_type( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + ) -> torch.Tensor: + """`(B, k_max, emb_dim)` per-slot type logits (`target="onehot"`) or + vectors (`target="embedding"`) — only under `generator in ("flow", + "ddpm")`; `generator == "wgan"` folds the type slice into `forward`'s + own output instead (see class docstring).""" + if self.type_head is None: + raise RuntimeError( + "this Stage2OneShot has no type_head — either " + "particle_type.target='physical' (the type slice is part of " + "forward()'s own output) or generator='wgan' (the WGAN " + "trainer reads the type slice out of forward()'s output " + "directly instead)" + ) + c_emb = self._cond_embed(cond_cont, cond_cat, stage1_out) + return self.type_head(c_emb).view(-1, self._type_k_max, self._type_emb_dim) + class Stage2Autoregressive(nn.Module): """Not implemented until v0.3.0 steps 4-7 (docs/v0.3.0-design.md §6, §12) @@ -1201,6 +1315,10 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: gen_sub = s2cfg.get(generator, {}) or {} legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") k_max = s2cfg.get("k_max", K_MAX) + particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} + sec_dim = stage2_trunk_sec_dim( + particle_type_cfg, generator, k_max, particle_cfg["emb_dim"] + ) result["stage2"] = Stage2OneShot( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, @@ -1210,7 +1328,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: n_res_blocks=s2cfg.get("n_res_blocks", 6), cond_out_dim=cond_out_dim, context_dim=s2cfg.get("context_dim", 64), - sec_dim=k_max * SEC_SLOT_DIM, + sec_dim=sec_dim, dropout=s2cfg.get("dropout", 0.0), generator=generator, time_dim=gen_sub.get("time_dim", 64), @@ -1218,6 +1336,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: k_max=k_max, router=stage2_router, build_n_sec_head=legacy_owner != "stage1", + particle_type_cfg=particle_type_cfg, ) return result @@ -1264,12 +1383,16 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: and s2cfg.get("decoder", "one_shot") != "autoregressive" ): k_max = s2cfg.get("k_max", K_MAX) + particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} + in_dim = stage2_trunk_sec_dim( + particle_type_cfg, "wgan", k_max, particle_cfg["emb_dim"] + ) result["stage2"] = CriticModel( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, particle_cfg=particle_cfg, material_cfg=material_cfg, - in_dim=k_max * SEC_SLOT_DIM, + in_dim=in_dim, hidden_dim=s2cfg.get("hidden_dim", 256), n_res_blocks=s2cfg.get("n_res_blocks", 6), cond_out_dim=cond_out_dim, diff --git a/giant/model/schedule.py b/giant/model/schedule.py index d9f437a..0a5bf3d 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -78,25 +78,40 @@ def flow_matching_loss_secondary( cond_cat: torch.Tensor, stage1_out: torch.Tensor, sec_mask: torch.Tensor, + type_dim: int | None = None, ) -> torch.Tensor: """Flow matching loss for the secondary decoder with per-slot masking. - x1: (B, SEC_DIM) — flattened secondary target (stick_logit, dir, log_mass, charge) + x1: (B, K_MAX * (CONT_SLOT_DIM + type_dim)) — flattened secondary target + (stick_logit, dir, then a `type_dim`-wide type slice) sec_mask: (B, K_MAX) bool — True for valid secondary slots + type_dim: width of the per-slot type slice folded into `x1` — defaults to + `PARTICLE_PHYS_DIM` (log_mass, charge), `particle_type.target = + "physical"`'s width and the only case this function handled before + v0.3.0 step 4. `0` means no type slice is in `x1` at all (`target` + in `("onehot", "embedding")` under `generator in ("flow", "ddpm")` — + see docs/v0.3.0-design.md decision 2, `Stage2OneShot.type_head` + handles the type loss separately in that case). Only valid-slot dimensions contribute to the loss; padded slots are zeroed before averaging, so the loss is not diluted by empty slots. Each slot packs CONT_SLOT_DIM continuous dims (stick_logit, dir) followed - by PARTICLE_PHYS_DIM physical-identity dims (log_mass, charge) — the - secondary's predicted physical identity, a fixed regression target (see - giant.data.transforms.encode_secondaries). Even though the two blocks are - the same order of magnitude now (unlike the 16-wide learned embedding - block this replaced), they're still on different physical scales, so - they're each averaged over their own width first and then combined with - equal weight — this stays correct if PARTICLE_PHYS_DIM/CONT_SLOT_DIM change. + by the `type_dim`-wide type slice — under `target = "physical"` (the + default) that's the secondary's predicted physical identity, a fixed + regression target (see giant.data.transforms.encode_secondaries); under + `target = "embedding"` (folded in only for `generator = "wgan"`, so + `type_dim > 0` here only ever means "physical") it would be the detached + embedding-table row. Even though the two blocks are the same order of + magnitude now (unlike the 16-wide learned embedding block "physical" + replaced), they're still on different physical scales, so they're each + averaged over their own width first and then combined with equal weight + — this stays correct if type_dim/CONT_SLOT_DIM change. """ - from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_SLOT_DIM + from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM + + if type_dim is None: + type_dim = PARTICLE_PHYS_DIM B = x1.size(0) t = torch.rand(B, device=x1.device) @@ -105,12 +120,15 @@ def flow_matching_loss_secondary( u_t = x1 - x0 v_t = model(x_t, cond_cont, cond_cat, stage1_out, t=t) - err = ((v_t - u_t) ** 2).view(B, K_MAX, SEC_SLOT_DIM) + slot_dim = CONT_SLOT_DIM + type_dim + err = ((v_t - u_t) ** 2).view(B, K_MAX, slot_dim) cont_err = err[..., :CONT_SLOT_DIM].mean(dim=-1) # (B, K_MAX) - phys_err = err[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM].mean(dim=-1) mask = sec_mask.float() denom = mask.sum().clamp(min=1) cont_loss = (cont_err * mask).sum() / denom + if type_dim == 0: + return cont_loss + phys_err = err[..., CONT_SLOT_DIM : CONT_SLOT_DIM + type_dim].mean(dim=-1) phys_loss = (phys_err * mask).sum() / denom return cont_loss + phys_loss diff --git a/giant/pipeline.py b/giant/pipeline.py index dfb2111..5584996 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -15,11 +15,14 @@ from giant.constants import ( ) from giant.data import setup_cache from giant.data.loader import ( + TopNMap, event_id_offset, find_parquet_files, iter_file_chunks, build_index_maps_from_files, + build_pdg_topn_map_from_files, build_process_map_from_files, + build_topn_map_from_files, ) from giant.data.transforms import ( Normalizer, @@ -46,6 +49,8 @@ class SetupStageResult: pdg_map: dict[int, int] mat_map: dict[str, int] proc_map: dict[str, int] | None + pdg_topn_map: TopNMap | None + mat_topn_map: TopNMap | None cond_norm: Normalizer tgt_norm: Normalizer sec_phys_norm: Normalizer @@ -193,6 +198,58 @@ def run_setup_stage( if cache is not None: cache.proc_maps[n_experts] = proc_map + # Top-N-plus-other maps for onehot conditioning/type axes + # (docs/v0.3.0-design.md §8). The PDG axis is shared by + # conditioning.particle.type="onehot" and + # stage2_model.particle_type.target="onehot" (both key off + # conditioning.particle.emb_dim), so at most one PDG scan is needed even + # if both consumers are active. The material axis is independent. + particle_cfg = cfg["conditioning"]["particle"] + material_cfg = cfg["conditioning"]["material"] + particle_type_target = cfg["stage2_model"].get("particle_type", {}).get("target") + + pdg_topn_map: TopNMap | None = None + if particle_cfg["type"] == "onehot" or particle_type_target == "onehot": + n_classes = particle_cfg["emb_dim"] + cache_key = setup_cache.topn_key("pdg", n_classes) + cached = cache.topn_maps.get(cache_key) if cache is not None else None + if cached is not None: + pdg_topn_map = cached + echo( + f"pdg top-N map: cache hit ({len(pdg_topn_map.class_map)} codes, " + f"{n_classes} classes)" + ) + else: + echo("building pdg top-N map …") + pdg_topn_map = build_pdg_topn_map_from_files(files, n_classes=n_classes) + echo( + f" {len(pdg_topn_map.class_map)} pdg codes mapped to {n_classes} classes" + ) + if cache is not None: + cache.topn_maps[cache_key] = pdg_topn_map + + mat_topn_map: TopNMap | None = None + if material_cfg["type"] == "onehot": + n_classes = material_cfg["emb_dim"] + cache_key = setup_cache.topn_key("material", n_classes) + cached = cache.topn_maps.get(cache_key) if cache is not None else None + if cached is not None: + mat_topn_map = cached + echo( + f"material top-N map: cache hit ({len(mat_topn_map.class_map)} " + f"materials, {n_classes} classes)" + ) + else: + echo("building material top-N map …") + mat_topn_map = build_topn_map_from_files( + files, "material", n_classes=n_classes, cast=str + ) + echo( + f" {len(mat_topn_map.class_map)} materials mapped to {n_classes} classes" + ) + if cache is not None: + cache.topn_maps[cache_key] = mat_topn_map + energy_router_active = any( r.get("enabled") and r.get("type") == "energy" for r in (stage1_router, stage2_router) @@ -232,14 +289,16 @@ def run_setup_stage( if not mask.any(): continue chunk_tr = {k: v[mask] for k, v in chunk.items()} - cond_cont, _, target_s1, n_sec, sec_cont, _proc, _, _ = build_features( - chunk_tr, - pdg_map, - mat_map, - proc_map=proc_map, - require_secondaries=True, - conditioning=conditioning, - sec_phys_only=True, + cond_cont, _, target_s1, n_sec, sec_cont, _proc, _, _, _ = ( + build_features( + chunk_tr, + pdg_map, + mat_map, + proc_map=proc_map, + require_secondaries=True, + conditioning=conditioning, + sec_phys_only=True, + ) ) cond_acc.update(cond_cont) tgt_acc.update(target_s1) @@ -273,6 +332,8 @@ def run_setup_stage( pdg_map=pdg_map, mat_map=mat_map, proc_map=proc_map, + pdg_topn_map=pdg_topn_map, + mat_topn_map=mat_topn_map, cond_norm=cond_norm, tgt_norm=tgt_norm, sec_phys_norm=sec_phys_norm, @@ -337,6 +398,35 @@ def run_train_job( setup.n_train_steps, ) + # cond_cat's onehot columns (docs/v0.3.0-design.md decision 4) are + # present only under conditioning="onehot" — validate_config enforces + # conditioning.particle.type == conditioning.material.type, so both + # axes' topN columns are always present or absent together. run_setup_stage + # builds both maps whenever conditioning=="onehot" (see its own + # particle_cfg["type"] == "onehot" check), so they're guaranteed non-None + # here — asserted, not just assumed, so a future wiring bug fails loudly + # instead of silently dropping the onehot columns. + cond_pdg_topn = None + cond_mat_topn = None + if conditioning == "onehot": + assert setup.pdg_topn_map is not None and setup.mat_topn_map is not None + cond_pdg_topn = setup.pdg_topn_map.class_map + cond_mat_topn = setup.mat_topn_map.class_map + + # The secondary type-index map depends on stage2_model.particle_type.target, + # independently of conditioning's own onehot/embedding choice above + # (docs/v0.3.0-design.md §3.3 — physical stays untouched/None). + particle_type_target = ( + cfg["stage2_model"].get("particle_type", {}).get("target", "physical") + ) + if particle_type_target == "onehot": + assert setup.pdg_topn_map is not None + sec_type_class_map = setup.pdg_topn_map.class_map + elif particle_type_target == "embedding": + sec_type_class_map = pdg_map + else: + sec_type_class_map = None + total_train_batches = n_train_steps // t["batch_size"] echo(f" ~{n_train_steps:,} train steps, ~{total_train_batches:,} batches") @@ -353,6 +443,9 @@ def run_train_job( proc_map=proc_map, conditioning=conditioning, sec_phys_normalizer=sec_phys_norm, + pdg_topn_map=cond_pdg_topn, + mat_topn_map=cond_mat_topn, + sec_type_class_map=sec_type_class_map, ) val_ds = StreamingStepsDataset( files=files, @@ -366,6 +459,9 @@ def run_train_job( proc_map=proc_map, conditioning=conditioning, sec_phys_normalizer=sec_phys_norm, + pdg_topn_map=cond_pdg_topn, + mat_topn_map=cond_mat_topn, + sec_type_class_map=sec_type_class_map, ) pin = device.type == "cuda" @@ -424,6 +520,8 @@ def run_train_job( pdg_map={str(k): v for k, v in pdg_map.items()}, mat_map={str(k): v for k, v in mat_map.items()}, proc_map=proc_map, + pdg_topn_map=setup.pdg_topn_map, + mat_topn_map=setup.mat_topn_map, model_config=model_config, resume_path=resume, total_train_batches=total_train_batches, diff --git a/giant/train.py b/giant/train.py index 2e1688c..53403c9 100644 --- a/giant/train.py +++ b/giant/train.py @@ -16,8 +16,10 @@ import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm -from giant.constants import K_MAX -from giant.model.network import Router +from giant.constants import CONT_SLOT_DIM, K_MAX +from giant.data.loader import TopNMap +from giant.data.setup_cache import topnmap_to_json +from giant.model.network import Router, stage2_type_dim from giant.model.schedule import ( CosineSchedule, flow_matching_loss, @@ -100,6 +102,61 @@ def _batch_to_device(batch: tuple, device: torch.device) -> tuple: return tuple(t.to(device) for t in batch) +def _assemble_stage2_real( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + generator: str, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """Ground-truth flattened stage-2 vector, matching whatever width + `Stage2OneShot`'s own trunk produces for this (target, generator) + combination (`giant.model.network.stage2_trunk_sec_dim`; + docs/v0.3.0-design.md decision 2/3): + + - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, + dir, log_mass, charge) flattened as-is. + - `target` in `("onehot", "embedding")` + `generator in ("flow", "ddpm")`: + just the continuous stick/dir slots — the type slice isn't part of + this vector at all (`Stage2OneShot.type_head` handles it separately). + - `target` in `("onehot", "embedding")` + `generator == "wgan"`: stick/dir + slots concatenated with the per-slot type vector — a one-hot of the + true class (`"onehot"`, relaxed on the *generated* side only, by the + caller) or the conditioning's own detached embedding-table row + (`"embedding"`, already continuous — no relaxation needed either side, + §2.1). + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return sec_cont.flatten(1) + cont = sec_cont[..., :CONT_SLOT_DIM] + if generator != "wgan": + return cont.flatten(1) + if target == "onehot": + type_vec = F.one_hot(sec_type_idx, num_classes=emb_dim).float() + else: + type_vec = cond_enc.pdg_emb(sec_type_idx).detach() + return torch.cat([cont, type_vec], dim=-1).flatten(1) + + +def _relax_onehot_type_slice( + x_flat: torch.Tensor, k_max: int, cont_dim: int, type_dim: int, tau: float +) -> torch.Tensor: + """Straight-through Gumbel-softmax relaxation of the per-slot type slice + inside a flattened `(B, k_max * (cont_dim + type_dim))` WGAN generator + output — decision 5 (docs/v0.3.0-design.md §2.1): the forward pass is a + hard one-hot (matching what the critic sees from real data), the + backward pass flows smooth gradient. Continuous slots (stick/dir, and + the type slice itself under `target = "embedding"`, which never calls + this) pass through unchanged.""" + B = x_flat.size(0) + x = x_flat.view(B, k_max, cont_dim + type_dim) + cont, type_logits = x[..., :cont_dim], x[..., cont_dim:] + type_soft = F.gumbel_softmax(type_logits, tau=tau, hard=True, dim=-1) + return torch.cat([cont, type_soft], dim=-1).reshape(B, -1) + + class StageTrainer: """One active stage's optimizer(s), EMA, and per-batch step. @@ -175,6 +232,8 @@ class FlowDDPMStageTrainer(StageTrainer): steps_per_epoch: int, ddpm_n_steps: int, device: torch.device, + particle_type_cfg: dict | None = None, + particle_type_emb_dim: int = 16, ) -> None: if is_stage2 and generator not in ("flow",): raise NotImplementedError( @@ -197,6 +256,20 @@ class FlowDDPMStageTrainer(StageTrainer): self.ema_decay = ema_decay self.router = _stage_router(self.model) + self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) + self.particle_type_emb_dim = particle_type_emb_dim + self.particle_type_lambda = self.particle_type_cfg.get("lambda", 1.0) + # Width of the type slice actually folded into x1_s2 by + # _assemble_stage2_real, under this trainer's generator (flow/ddpm + # only — see the NotImplementedError above): "physical" keeps it + # folded in (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/ + # "embedding" pull it out into model.type_head instead (0 here). + self._flow_type_dim = ( + None + if self.particle_type_cfg.get("target", "physical") == "physical" + else 0 + ) + self.params = list(self.model.parameters()) self.optimizer = optim.AdamW(self.params, lr=lr, weight_decay=weight_decay) warmup_steps = warmup_epochs * steps_per_epoch @@ -236,16 +309,69 @@ class FlowDDPMStageTrainer(StageTrainer): assert self.ddpm_schedule is not None return self.ddpm_schedule.loss(self.model, x1_s1, cond_cont, cond_cat) return flow_matching_loss_secondary( - self.model, x1_s2, cond_cont, cond_cat, stage1_ctx, sec_mask + self.model, + x1_s2, + cond_cont, + cond_cat, + stage1_ctx, + sec_mask, + type_dim=self._flow_type_dim, ) + def _type_loss( + self, cond_cont, cond_cat, stage1_ctx, sec_type_idx, sec_mask, device + ): + """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for + `Stage2OneShot.type_head` — the non-adversarial counterpart to + WGANStageTrainer's ST-Gumbel-into-the-critic path (decision 2/5). + Zero when this stage has no `type_head` (stage 1, or + `particle_type.target = "physical"`).""" + l_type = torch.zeros((), device=device) + type_acc = torch.zeros((), device=device) + type_head = getattr(self.model, "type_head", None) + if not self.is_stage2 or type_head is None: + return l_type, type_acc + type_out = self.model.predict_type(cond_cont, cond_cat, stage1_ctx) + mask = sec_mask.float() + denom = mask.sum().clamp(min=1) + if self.particle_type_cfg.get("target") == "onehot": + ce = F.cross_entropy( + type_out.transpose(1, 2), sec_type_idx, reduction="none" + ) + l_type = (ce * mask).sum() / denom + type_acc = ( + (type_out.argmax(-1) == sec_type_idx).float() * mask + ).sum() / denom + else: # "embedding" + target_vec = self.model.cond_enc.pdg_emb(sec_type_idx).detach() + se = ((type_out - target_vec) ** 2).mean(-1) + l_type = (se * mask).sum() / denom + return l_type, type_acc + def _compute(self, batch: tuple, device: torch.device) -> dict: - cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx = _batch_to_device( - batch, device - ) - x1_s2 = sec_cont.flatten(1) + ( + cond_cont, + cond_cat, + x1_s1, + n_sec, + sec_cont, + proc_idx, + sec_type_idx, + ) = _batch_to_device(batch, device) sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) stage1_ctx = x1_s1.detach() + x1_s2 = ( + _assemble_stage2_real( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.generator, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + if self.is_stage2 + else None + ) l_gen = self._generator_loss( cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx @@ -257,13 +383,21 @@ class FlowDDPMStageTrainer(StageTrainer): l_nsec = F.cross_entropy(n_sec_logits, n_sec) nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + l_type, type_acc = self._type_loss( + cond_cont, cond_cat, stage1_ctx, sec_type_idx, sec_mask, device + ) + l_balance = l_proc = l_entropy = torch.zeros((), device=device) if self.router is not None: l_balance = self.router.balance_loss(cond_cont, cond_cat) l_proc = self.router.classify_loss(cond_cont, cond_cat, proc_idx) l_entropy = self.router.entropy_loss(cond_cont, cond_cat) - total = self.lambda_weight * l_gen + self.n_sec_lambda * l_nsec + total = ( + self.lambda_weight * l_gen + + self.n_sec_lambda * l_nsec + + self.particle_type_lambda * l_type + ) if self.lambda_balance > 0: total = total + self.lambda_balance * l_balance if self.lambda_proc > 0: @@ -275,6 +409,8 @@ class FlowDDPMStageTrainer(StageTrainer): "total": total, "loss_gen": l_gen, "loss_nsec": l_nsec, + "loss_type": l_type, + "type_acc": type_acc, "loss_balance": l_balance, "loss_proc": l_proc, "loss_entropy": l_entropy, @@ -302,6 +438,8 @@ class FlowDDPMStageTrainer(StageTrainer): "loss": out["total"].item(), "loss_gen": out["loss_gen"].item(), "loss_nsec": out["loss_nsec"].item(), + "loss_type": out["loss_type"].item(), + "type_acc": out["type_acc"].item(), "loss_balance": out["loss_balance"].item(), "loss_proc": out["loss_proc"].item(), "loss_entropy": out["loss_entropy"].item(), @@ -318,6 +456,8 @@ class FlowDDPMStageTrainer(StageTrainer): "loss": out["total"].item(), "loss_gen": out["loss_gen"].item(), "loss_nsec": out["loss_nsec"].item(), + "loss_type": out["loss_type"].item(), + "type_acc": out["type_acc"].item(), "loss_balance": out["loss_balance"].item(), "loss_proc": out["loss_proc"].item(), "loss_entropy": out["loss_entropy"].item(), @@ -387,6 +527,10 @@ class WGANStageTrainer(StageTrainer): epochs: int, steps_per_epoch: int, device: torch.device, + particle_type_cfg: dict | None = None, + particle_type_emb_dim: int = 16, + type_gumbel_tau_start: float = 1.0, + type_gumbel_tau_end: float = 0.1, ) -> None: self.name = name self.is_stage2 = is_stage2 @@ -400,6 +544,11 @@ class WGANStageTrainer(StageTrainer): self.ema_decay = ema_decay self.router = _stage_router(self.model) + self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) + self.particle_type_emb_dim = particle_type_emb_dim + self.type_gumbel_tau_start = type_gumbel_tau_start + self.type_gumbel_tau_end = type_gumbel_tau_end + self.g_params = list(self.model.parameters()) self.d_params = list(self.critic.parameters()) # WGAN-GP recipe (Gulrajani et al. 2017): Adam, beta1=0, no weight decay. @@ -423,6 +572,7 @@ class WGANStageTrainer(StageTrainer): self._lr_lambda = _lr_lambda self.lr_sched = optim.lr_scheduler.LambdaLR(self.optimizer, _lr_lambda) + self.total_steps = total_steps self.ema_model: torch.nn.Module | None = None if ema_decay > 0: @@ -431,9 +581,15 @@ class WGANStageTrainer(StageTrainer): p.requires_grad_(False) def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: - cond_cont, cond_cat, x1_s1, n_sec, sec_cont, _proc_idx = _batch_to_device( - batch, device - ) + ( + cond_cont, + cond_cat, + x1_s1, + n_sec, + sec_cont, + _proc_idx, + sec_type_idx, + ) = _batch_to_device(batch, device) B = cond_cont.size(0) stage1_ctx = x1_s1.detach() @@ -447,24 +603,49 @@ class WGANStageTrainer(StageTrainer): fake = self.model(z, cond_cont, cond_cat) mask = None else: - from giant.constants import SEC_SLOT_DIM + target = self.particle_type_cfg.get("target", "physical") + type_dim = stage2_type_dim( + self.particle_type_cfg, self.particle_type_emb_dim + ) + slot_width = CONT_SLOT_DIM + type_dim sec_mask = torch.arange(K_MAX, device=device).unsqueeze( 0 ) < n_sec.unsqueeze(1) mask = ( - sec_mask.unsqueeze(-1) - .expand(-1, -1, SEC_SLOT_DIM) - .reshape(B, -1) - .float() + sec_mask.unsqueeze(-1).expand(-1, -1, slot_width).reshape(B, -1).float() + ) + real = ( + _assemble_stage2_real( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + "wgan", + self.model.cond_enc, + self.particle_type_emb_dim, + ) + * mask ) - real = sec_cont.flatten(1) * mask def critic_fn(x): return self.critic(x, cond_cont, cond_cat, stage1_ctx) z = torch.randn(B, self.model.noise_dim, device=device) fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) + if target == "onehot": + # Straight-through Gumbel-softmax relaxation of the type + # slice only (decision 5) — the critic must see a hard + # one-hot forward (matching what "real" data looks like) + # while gradient still flows smoothly to the generator. + tau = _gumbel_tau( + global_step, + self.total_steps, + self.type_gumbel_tau_start, + self.type_gumbel_tau_end, + ) + fake_raw = _relax_onehot_type_slice( + fake_raw, K_MAX, CONT_SLOT_DIM, type_dim, tau + ) fake = fake_raw * mask # --- critic step (every batch) --- @@ -635,6 +816,10 @@ def _build_stage_trainers( lambda_entropy = router_cfg.get("lambda_entropy", 0.0) gumbel_tau_start = router_cfg.get("gumbel_tau_start", 1.0) gumbel_tau_end = router_cfg.get("gumbel_tau_end", 0.1) + particle_type_cfg = cfg["stage2_model"].get("particle_type") or { + "target": "physical" + } + particle_type_emb_dim = cfg["conditioning"]["particle"]["emb_dim"] if generator == "wgan": critic = critics.get(name) @@ -659,6 +844,10 @@ def _build_stage_trainers( epochs=t["epochs"], steps_per_epoch=max(total_train_batches, 1), device=device, + particle_type_cfg=particle_type_cfg, + particle_type_emb_dim=particle_type_emb_dim, + type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), + type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), ) else: ddpm_n_steps = stage_cfg.get("ddpm", {}).get("n_steps", 1000) @@ -682,6 +871,8 @@ def _build_stage_trainers( steps_per_epoch=max(total_train_batches, 1), ddpm_n_steps=ddpm_n_steps, device=device, + particle_type_cfg=particle_type_cfg, + particle_type_emb_dim=particle_type_emb_dim, ) return trainers @@ -710,11 +901,15 @@ def _metrics_fields(trainers: dict[str, StageTrainer]) -> list[str]: f"{name}_train_loss_proc", f"{name}_train_loss_entropy", f"{name}_train_nsec_acc", + f"{name}_train_loss_type", + f"{name}_train_type_acc", f"{name}_train_grad_norm", f"{name}_val_loss", f"{name}_val_loss_gen", f"{name}_val_loss_nsec", f"{name}_val_nsec_acc", + f"{name}_val_loss_type", + f"{name}_val_type_acc", ] if trainer.router is not None: fields += [ @@ -766,6 +961,8 @@ def train( pdg_map: dict | None = None, mat_map: dict | None = None, proc_map: dict | None = None, + pdg_topn_map: TopNMap | None = None, + mat_topn_map: TopNMap | None = None, model_config: dict | None = None, resume_path: str | Path | None = None, total_train_batches: int = 0, @@ -842,6 +1039,10 @@ def train( ckpt["mat_map"] = mat_map if proc_map is not None: ckpt["proc_map"] = proc_map + if pdg_topn_map is not None: + ckpt["pdg_topn_map"] = topnmap_to_json(pdg_topn_map) + if mat_topn_map is not None: + ckpt["mat_topn_map"] = topnmap_to_json(mat_topn_map) if model_config is not None: ckpt["model_config"] = model_config return ckpt @@ -1183,6 +1384,12 @@ def train( metrics_row[f"{name}_train_nsec_acc"] = ( sums.get("nsec_acc", 0.0) / n_train ) + metrics_row[f"{name}_train_loss_type"] = ( + sums.get("loss_type", 0.0) / n_train + ) + metrics_row[f"{name}_train_type_acc"] = ( + sums.get("type_acc", 0.0) / n_train + ) metrics_row[f"{name}_train_grad_norm"] = ( sums.get("grad_norm", 0.0) / n_train ) @@ -1192,6 +1399,10 @@ def train( v.get("loss_nsec", 0.0) / n_val ) metrics_row[f"{name}_val_nsec_acc"] = v.get("nsec_acc", 0.0) / n_val + metrics_row[f"{name}_val_loss_type"] = ( + v.get("loss_type", 0.0) / n_val + ) + metrics_row[f"{name}_val_type_acc"] = v.get("type_acc", 0.0) / n_val grad_norm_total += sums.get("grad_norm", 0.0) / n_train if tr.router is not None: rs = val_router_sums.get(name) diff --git a/tests/test_cli_predict.py b/tests/test_cli_predict.py index a6f74fb..8bae93f 100644 --- a/tests/test_cli_predict.py +++ b/tests/test_cli_predict.py @@ -1,14 +1,72 @@ import uuid +import pytest +import typer import yaml from giant.cli import ( _CEPH_PREDICTIONS, + _check_v030_onehot_support, _resolve_prediction_output, _write_prediction_ref, ) +# --------------------------------------------------------------------------- +# _check_v030_onehot_support +# --------------------------------------------------------------------------- + + +def _nested_model_cfg( + particle_type="physical", material_type="physical", target="physical" +): + return { + "conditioning": { + "particle": {"type": particle_type, "emb_dim": 8}, + "material": {"type": material_type, "emb_dim": 8}, + }, + "stage2_model": {"particle_type": {"target": target}}, + } + + +def test_check_v030_onehot_support_allows_physical(): + _check_v030_onehot_support(_nested_model_cfg(), "predict") # no raise + + +def test_check_v030_onehot_support_rejects_onehot_particle_conditioning(): + cfg = _nested_model_cfg(particle_type="onehot") + with pytest.raises(typer.Exit): + _check_v030_onehot_support(cfg, "predict") + + +def test_check_v030_onehot_support_rejects_onehot_material_conditioning(): + cfg = _nested_model_cfg(material_type="onehot") + with pytest.raises(typer.Exit): + _check_v030_onehot_support(cfg, "rollout") + + +def test_check_v030_onehot_support_rejects_onehot_particle_type_target(): + cfg = _nested_model_cfg(target="onehot") + with pytest.raises(typer.Exit): + _check_v030_onehot_support(cfg, "predict") + + +def test_check_v030_onehot_support_rejects_embedding_particle_type_target(): + cfg = _nested_model_cfg( + particle_type="embedding", material_type="embedding", target="embedding" + ) + with pytest.raises(typer.Exit): + _check_v030_onehot_support(cfg, "predict") + + +def test_check_v030_onehot_support_is_noop_for_v02_flat_model_config(): + """A v0.2 checkpoint's flat model_config has conditioning as a plain + string, not a dict — never onehot/embedding-target, so this must be a + silent no-op rather than crash on `.get("particle")` against a string.""" + cfg = {"conditioning": "embedding", "mode": "flow"} + _check_v030_onehot_support(cfg, "predict") # no raise + + # --------------------------------------------------------------------------- # _resolve_prediction_output # --------------------------------------------------------------------------- diff --git a/tests/test_loader.py b/tests/test_loader.py index f0900c7..23003a8 100644 --- a/tests/test_loader.py +++ b/tests/test_loader.py @@ -6,7 +6,9 @@ from giant.data.loader import ( EVENT_ID_FILE_STRIDE, build_index_maps, build_index_maps_from_files, + build_pdg_topn_map_from_files, build_process_map_from_files, + build_topn_map_from_files, event_id_offset, find_parquet_files, iter_cond_chunks, @@ -178,6 +180,64 @@ def test_build_process_map_from_files_three_files_partial_overlap(tmp_path): assert proc_map["compt"] == 2 +# ── build_topn_map_from_files / build_pdg_topn_map_from_files ────────────── + + +def test_build_topn_map_from_files_keeps_most_frequent(tmp_path): + materials = ["G4_AIR"] * 5 + ["PbWO4"] * 3 + ["G4_Fe"] * 2 + ["G4_Pb"] * 1 + path = tmp_path / "a.parquet" + pd.DataFrame({"material": materials}).to_parquet(path) + + m = build_topn_map_from_files([path], "material", n_classes=3, cast=str) + + assert m.class_map["G4_AIR"] == 0 + assert m.class_map["PbWO4"] == 1 + assert m.class_map["G4_Fe"] == 2 # "other" (n_classes - 1) + assert m.class_map["G4_Pb"] == 2 + assert m.other_members == {"G4_Fe": 2, "G4_Pb": 1} + + +def test_build_topn_map_from_files_fewer_values_than_n_classes(tmp_path): + path = tmp_path / "a.parquet" + pd.DataFrame({"material": ["G4_AIR", "PbWO4"]}).to_parquet(path) + + m = build_topn_map_from_files([path], "material", n_classes=5, cast=str) + + assert m.class_map == {"G4_AIR": 0, "PbWO4": 1} + assert m.other_members == {} + + +def test_build_pdg_topn_map_from_files_pools_primary_and_secondary_pdg(tmp_path): + """A species that's rare as a primary but common as a secondary must + still rank by its pooled (primary + secondary) count, not just its + primary-role count alone — the whole point of pooling both roles + (docs/v0.3.0-design.md §8).""" + path = tmp_path / "a.parquet" + # primary pdg: mostly 11 (electron), one lone 22 (photon) + pdg = [11] * 5 + [22] * 1 + # secondaries: 22 (photon) appears often as a secondary despite being + # rare as a primary above + sec_pdg_list = [[22, 22]] * 5 + [[]] * 1 + pd.DataFrame({"pdg": pdg, "sec_pdg_list": sec_pdg_list}).to_parquet(path) + + m = build_pdg_topn_map_from_files([path], n_classes=3) + + # pooled: 11 -> 5, 22 -> 1 (primary) + 10 (secondary) = 11 + assert m.class_map[22] == 0 + assert m.class_map[11] == 1 + + +def test_build_pdg_topn_map_from_files_missing_sec_pdg_list_column(tmp_path): + """Files predating the parent->child join have no sec_pdg_list column — + must not raise, just count the primary pdg column alone.""" + path = tmp_path / "a.parquet" + pd.DataFrame({"pdg": [11, 11, 22]}).to_parquet(path) + + m = build_pdg_topn_map_from_files([path], n_classes=3) + + assert m.class_map == {11: 0, 22: 1} + + # ── build_index_maps (in-memory) ──────────────────────────────────────────── diff --git a/tests/test_network.py b/tests/test_network.py index 0bb99b5..2117898 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -1,9 +1,19 @@ import torch -from giant.constants import COND_DIM -from giant.model.network import SinusoidalEmbedding, Stage1Model +from giant.constants import CONT_SLOT_DIM, COND_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM +from giant.model.network import ( + ConditionEncoder, + SinusoidalEmbedding, + Stage1Model, + Stage2OneShot, + cat_col_layout, + stage2_trunk_sec_dim, + stage2_type_dim, +) PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +ONEHOT_PARTICLE_CFG = {"type": "onehot", "emb_dim": 6, "n_layers": 1} +ONEHOT_MATERIAL_CFG = {"type": "onehot", "emb_dim": 4, "n_layers": 1} def test_sinusoidal_embedding_shape(): @@ -71,3 +81,212 @@ def test_stage1_model_no_n_sec_head_by_default(): pdg_vocab=3, mat_vocab=2, particle_cfg=PARTICLE_CFG, material_cfg=MATERIAL_CFG ) assert model.n_sec_head is None + + +# --- cat_col_layout / stage2_type_dim / stage2_trunk_sec_dim --------------- + + +def test_cat_col_layout_neither_onehot(): + assert cat_col_layout("physical", "embedding") == (None, None) + + +def test_cat_col_layout_particle_only(): + assert cat_col_layout("onehot", "physical") == (2, None) + + +def test_cat_col_layout_material_only(): + assert cat_col_layout("physical", "onehot") == (None, 2) + + +def test_cat_col_layout_both_onehot_particle_then_material(): + assert cat_col_layout("onehot", "onehot") == (2, 3) + + +def test_stage2_type_dim_physical_is_particle_phys_dim(): + assert stage2_type_dim({"target": "physical"}, emb_dim=16) == PARTICLE_PHYS_DIM + + +def test_stage2_type_dim_onehot_and_embedding_are_emb_dim(): + assert stage2_type_dim({"target": "onehot"}, emb_dim=16) == 16 + assert stage2_type_dim({"target": "embedding"}, emb_dim=16) == 16 + + +def test_stage2_trunk_sec_dim_physical_matches_v02_sec_dim(): + k_max = 15 + assert ( + stage2_trunk_sec_dim({"target": "physical"}, "flow", k_max, emb_dim=16) + == k_max * SEC_SLOT_DIM + ) + assert ( + stage2_trunk_sec_dim({"target": "physical"}, "wgan", k_max, emb_dim=16) + == k_max * SEC_SLOT_DIM + ) + + +def test_stage2_trunk_sec_dim_onehot_wgan_folds_type_in(): + k_max = 15 + assert stage2_trunk_sec_dim( + {"target": "onehot"}, "wgan", k_max, emb_dim=16 + ) == k_max * (CONT_SLOT_DIM + 16) + + +def test_stage2_trunk_sec_dim_onehot_flow_excludes_type(): + k_max = 15 + assert ( + stage2_trunk_sec_dim({"target": "onehot"}, "flow", k_max, emb_dim=16) + == k_max * CONT_SLOT_DIM + ) + + +# --- ConditionEncoder onehot mode ------------------------------------------- + + +def test_condition_encoder_onehot_forward_shape_and_gradients(): + B = 8 + particle_emb_dim = int(ONEHOT_PARTICLE_CFG["emb_dim"]) + material_emb_dim = int(ONEHOT_MATERIAL_CFG["emb_dim"]) + enc = ConditionEncoder( + pdg_vocab=5, + mat_vocab=3, + particle_cfg=ONEHOT_PARTICLE_CFG, + material_cfg=ONEHOT_MATERIAL_CFG, + out_dim=32, + ) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.stack( + [ + torch.randint(0, 5, (B,)), + torch.randint(0, 3, (B,)), + torch.randint(0, particle_emb_dim, (B,)), + torch.randint(0, material_emb_dim, (B,)), + ], + dim=1, + ) + out = enc(cond_cont, cond_cat) + assert out.shape == (B, 32) + # onehot itself is unlearned, but the fusion MLP downstream still has + # gradients — the encoder as a whole must still be trainable. + out.sum().backward() + assert enc.mlp[0].weight.grad is not None + + +def test_condition_encoder_onehot_is_a_true_one_hot_vector(): + """The onehot axis feeds a fixed, unlearned one-hot into the fusion MLP — + verify the concatenated input segment really is one-hot, not e.g. an + accidentally-learned embedding.""" + B = 4 + particle_emb_dim = int(ONEHOT_PARTICLE_CFG["emb_dim"]) + enc = ConditionEncoder( + pdg_vocab=5, + mat_vocab=3, + particle_cfg=ONEHOT_PARTICLE_CFG, + material_cfg={"type": "physical", "emb_dim": 4, "n_layers": 1}, + out_dim=16, + ) + cond_cont = torch.zeros(B, COND_DIM) + idx = torch.tensor([0, 1, 2, 5]) + cond_cat = torch.stack( + [ + torch.zeros(B, dtype=torch.long), + torch.zeros(B, dtype=torch.long), + idx.clamp(max=particle_emb_dim - 1), + ], + dim=1, + ) + pdg_e = enc._particle_embed(cond_cont, cond_cat) + assert pdg_e.shape == (B, particle_emb_dim) + assert torch.all(pdg_e.sum(dim=-1) == 1.0) + + +# --- Stage2OneShot particle_type architecture (docs/v0.3.0-design.md decision 2) -- + + +def _build_stage2(target: str, generator: str, emb_dim: int = 6) -> Stage2OneShot: + particle_cfg = {"type": "physical", "emb_dim": emb_dim, "n_layers": 1} + if target != "physical": + particle_cfg = dict(particle_cfg) + if target == "embedding": + particle_cfg["type"] = "embedding" + k_max = 5 + sec_dim = stage2_trunk_sec_dim({"target": target}, generator, k_max, emb_dim) + return Stage2OneShot( + pdg_vocab=5, + mat_vocab=3, + particle_cfg=particle_cfg, + material_cfg=MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=1, + cond_out_dim=16, + context_dim=8, + sec_dim=sec_dim, + generator=generator, + k_max=k_max, + particle_type_cfg={"target": target, "lambda": 1.0}, + ) + + +def test_stage2_oneshot_physical_has_no_type_head_regardless_of_generator(): + assert _build_stage2("physical", "flow").type_head is None + assert _build_stage2("physical", "wgan").type_head is None + + +def test_stage2_oneshot_onehot_flow_has_type_head(): + model = _build_stage2("onehot", "flow") + assert model.type_head is not None + + +def test_stage2_oneshot_onehot_wgan_has_no_type_head(): + """Under wgan the type slice is folded into forward()'s own output and + relaxed via ST-Gumbel by the trainer — no separate head needed.""" + model = _build_stage2("onehot", "wgan") + assert model.type_head is None + + +def test_stage2_oneshot_embedding_flow_has_type_head(): + model = _build_stage2("embedding", "flow") + assert model.type_head is not None + + +def test_stage2_oneshot_predict_type_shape(): + B, k_max, emb_dim = 4, 5, 6 + model = _build_stage2("onehot", "flow", emb_dim=emb_dim) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + out = model.predict_type(cond_cont, cond_cat, stage1_out) + assert out.shape == (B, k_max, emb_dim) + + +def test_stage2_oneshot_predict_type_raises_when_no_type_head(): + model = _build_stage2("physical", "flow") + cond_cont = torch.randn(2, COND_DIM) + cond_cat = torch.zeros(2, 2, dtype=torch.long) + stage1_out = torch.randn(2, 9) + try: + model.predict_type(cond_cont, cond_cat, stage1_out) + raise AssertionError("expected RuntimeError") + except RuntimeError: + pass + + +def test_stage2_oneshot_forward_shape_onehot_wgan(): + B, k_max, emb_dim = 4, 5, 6 + model = _build_stage2("onehot", "wgan", emb_dim=emb_dim) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + z = torch.randn(B, model.noise_dim) + out = model(z, cond_cont, cond_cat, stage1_out) + assert out.shape == (B, k_max * (CONT_SLOT_DIM + emb_dim)) + + +def test_stage2_oneshot_forward_shape_onehot_flow_excludes_type(): + B, k_max, emb_dim = 4, 5, 6 + model = _build_stage2("onehot", "flow", emb_dim=emb_dim) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + x_t = torch.randn(B, k_max * CONT_SLOT_DIM) + t = torch.rand(B) + out = model(x_t, cond_cont, cond_cat, stage1_out, t=t) + assert out.shape == (B, k_max * CONT_SLOT_DIM) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 833b8fd..f49c38d 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -152,6 +152,39 @@ def test_run_train_job_second_run_hits_cache(tmp_path, data, monkeypatch): assert "normalizer: cache hit" in joined +def test_run_train_job_builds_caches_and_persists_pdg_topn_map(tmp_path, data): + """DEFAULT_CONFIG's stage2_model.particle_type.target defaults to + "onehot" (docs/v0.3.0-design.md §3.3/§8) — a plain _tiny_cfg() run must + build the shared pdg top-N map, cache it in the setup-cache sidecar, and + persist it into the checkpoint, with no extra config needed.""" + echo1 = _run(data, tmp_path / "out1") + assert any("building pdg top-N map" in m for m in echo1) + + loaded = setup_cache.load(data, [data]) + assert loaded is not None + key = setup_cache.topn_key("pdg", 4) # conditioning.particle.emb_dim = 4 + assert key in loaded.topn_maps + assert set(loaded.topn_maps[key].class_map.keys()) >= {11, 22} + + ckpt = torch.load(tmp_path / "out1" / "last.pt", weights_only=False) + assert "pdg_topn_map" in ckpt + assert set(ckpt["pdg_topn_map"]["class_map"].keys()) >= {"11", "22"} + + echo2 = _run(data, tmp_path / "out2") + assert any("pdg top-N map: cache hit" in m for m in echo2) + + +def test_run_train_job_no_topn_map_for_physical_target(tmp_path, data): + cfg = _tiny_cfg() + cfg["stage2_model"]["particle_type"] = {"target": "physical", "lambda": 1.0} + echo = _run(data, tmp_path / "out", cfg=cfg) + assert not any("top-N map" in m for m in echo) + + loaded = setup_cache.load(data, [data]) + assert loaded is not None + assert loaded.topn_maps == {} + + def test_run_train_job_warns_when_num_workers_exceeds_shared_quota( tmp_path, data, monkeypatch ): diff --git a/tests/test_setup_cache.py b/tests/test_setup_cache.py index a243a53..9224b64 100644 --- a/tests/test_setup_cache.py +++ b/tests/test_setup_cache.py @@ -7,6 +7,7 @@ import pandas as pd import pytest from giant.data import setup_cache +from giant.data.loader import TopNMap from giant.data.setup_cache import NormalizerEntry, SetupCache from giant.data.transforms import Normalizer @@ -101,6 +102,32 @@ def test_save_load_round_trip(tmp_path): np.testing.assert_allclose(entry.energy_quantiles, [1.0, 2.0, 3.0]) +def test_save_load_round_trip_topn_maps(tmp_path): + data = _touch_parquet(tmp_path / "shard.parquet") + files = [data] + + cache = SetupCache.empty(files) + cache.topn_maps[setup_cache.topn_key("pdg", 3)] = TopNMap( + class_map={22: 0, 11: 1, 2212: 2}, other_members={2212: 5} + ) + cache.topn_maps[setup_cache.topn_key("material", 2)] = TopNMap( + class_map={"G4_AIR": 0, "PbWO4": 1}, other_members={} + ) + + setup_cache.save(data, files, cache) + loaded = setup_cache.load(data, files) + + assert loaded is not None + pdg_m = loaded.topn_maps[setup_cache.topn_key("pdg", 3)] + assert pdg_m.class_map == {22: 0, 11: 1, 2212: 2} + assert pdg_m.other_members == {2212: 5} + # key type is int (matches pdg_map's own key type), not str + assert all(isinstance(k, int) for k in pdg_m.class_map) + + mat_m = loaded.topn_maps[setup_cache.topn_key("material", 2)] + assert mat_m.class_map == {"G4_AIR": 0, "PbWO4": 1} + + def test_load_missing_sidecar_returns_none(tmp_path): data = _touch_parquet(tmp_path / "shard.parquet") assert setup_cache.load(data, [data]) is None diff --git a/tests/test_train.py b/tests/test_train.py index 5892de7..e20223d 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -153,7 +153,10 @@ def _fake_batches(n_batches, batch_size, seed=0): n_sec = torch.randint(0, K_MAX, (batch_size,), generator=g) sec_cont = torch.randn(batch_size, K_MAX, SEC_SLOT_DIM, generator=g) proc_idx = torch.zeros(batch_size, dtype=torch.long) - batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx)) + sec_type_idx = torch.zeros(batch_size, K_MAX, dtype=torch.long) + batches.append( + (cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx) + ) return batches @@ -234,6 +237,42 @@ def _run_train(cfg, out_dir, resume_path=None): }, ), ), + ( + "stage2_onehot_target_wgan", + lambda cfg: cfg["stage2_model"].__setitem__( + "particle_type", {"target": "onehot", "lambda": 1.0} + ), + ), + ( + "stage2_onehot_target_flow", + lambda cfg: ( + cfg["stage2_model"].__setitem__("generator", "flow"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "onehot", "lambda": 1.0} + ), + ), + ), + ( + "stage2_embedding_target_wgan", + lambda cfg: ( + cfg["conditioning"]["particle"].__setitem__("type", "embedding"), + cfg["conditioning"]["material"].__setitem__("type", "embedding"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "embedding", "lambda": 1.0} + ), + ), + ), + ( + "stage2_embedding_target_flow", + lambda cfg: ( + cfg["conditioning"]["particle"].__setitem__("type", "embedding"), + cfg["conditioning"]["material"].__setitem__("type", "embedding"), + cfg["stage2_model"].__setitem__("generator", "flow"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "embedding", "lambda": 1.0} + ), + ), + ), ], ) def test_train_end_to_end(label, mutate): diff --git a/tests/test_transforms.py b/tests/test_transforms.py index dcda475..a2e19c2 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -319,7 +319,7 @@ def test_build_features_clamps_n_sec_label_to_k_max(): pdg_map = {11: 0} mat_map = {"PbWO4": 0} - _, _, _, n_sec, _, _, _, _ = build_features(data, pdg_map, mat_map) + _, _, _, n_sec, _, _, _, _, _ = build_features(data, pdg_map, mat_map) assert n_sec.max() <= K_MAX np.testing.assert_array_equal(n_sec, [0, 5, K_MAX]) @@ -361,7 +361,7 @@ def test_build_features_proc_idx_zero_without_proc_map(): ) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} - *_, proc_idx, _, _ = build_features(data, pdg_map, mat_map) + *_, proc_idx, _, _, _ = build_features(data, pdg_map, mat_map) np.testing.assert_array_equal(proc_idx, [0, 0, 0]) @@ -373,7 +373,7 @@ def test_build_features_proc_idx_looks_up_proc_map(): pdg_map, mat_map = {11: 0}, {"PbWO4": 0} proc_map = {"compt": 0, "phot": 1, "eIoni": 2} - *_, proc_idx, _, _ = build_features(data, pdg_map, mat_map, proc_map=proc_map) + *_, proc_idx, _, _, _ = build_features(data, pdg_map, mat_map, proc_map=proc_map) np.testing.assert_array_equal(proc_idx, [0, 1, 2]) -- 2.39.5 From c9d255b1c59dac136a9e6a893409d3bdb817d2a8 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 7 Aug 2026 09:36:49 +0200 Subject: [PATCH 08/30] =?UTF-8?q?v0.3.0=20step=205:=20Stage2Autoregressive?= =?UTF-8?q?=20(history=3Dmarkov)=20+=20=C2=A711.4=20grad=20instrumentation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replaces the Stage2Autoregressive stub with a real per-token secondary decoder: MarkovHistory summarizes the previous secondary, remaining-energy fraction and slot index round out the per-token conditioning, and the existing Trunk/MonolithicTrunk/RoutedTrunk machinery is reused unchanged by batching all K_MAX tokens together under teacher forcing (one parallel pass, no new trunk code). build_models/build_critics wire it in; the WGAN critic stays whole-sequence, so build_critics needs no AR-specific path. train.py's FlowDDPMStageTrainer/WGANStageTrainer gain a decoder branch, sharing optimizer/EMA/checkpoint machinery with the one-shot path. _assemble_stage2_real is now defined in terms of the new unflattened _assemble_stage2_ar_target helper, removing a near-duplicate branch. Also lands the §11.4 differentiability validation-obligation instrumentation (trunk-gradient norm from the particle-type slice vs. the continuous slices, for generator=wgan + particle_type.target=onehot) via backward hooks in _relax_onehot_type_slice, decoder-agnostic and surfaced as two new metrics.csv columns. This also fixes the standing regression where any config not explicitly overriding decoder="one_shot" crashed at build_models, since stage2_model.decoder defaults to "autoregressive" — confirmed by removing tests/test_pipeline.py's now-stale override so the default config runs end-to-end against real synthetic data. Co-Authored-By: Claude Sonnet 5 --- giant/config.py | 16 ++ giant/model/network.py | 325 +++++++++++++++++++++++++++++++---- giant/model/schedule.py | 67 ++++++++ giant/train.py | 366 ++++++++++++++++++++++++++++++++++------ tests/test_config.py | 49 ++++++ tests/test_network.py | 228 +++++++++++++++++++++++++ tests/test_phase2.py | 118 ++++++++++++- tests/test_pipeline.py | 8 +- tests/test_train.py | 252 ++++++++++++++++++++++++++- 9 files changed, 1328 insertions(+), 101 deletions(-) diff --git a/giant/config.py b/giant/config.py index 1cb44f3..93972e6 100644 --- a/giant/config.py +++ b/giant/config.py @@ -733,6 +733,22 @@ def validate_config(cfg: dict) -> None: "(standalone stage-2 evaluation only, never for rollout)" ) + if _get_path(cfg, "stage2_model.decoder") == "autoregressive": + history = _get_path(cfg, "stage2_model.autoregressive.history") + if history != "markov": + raise ValueError( + f"stage2_model.autoregressive.history = {history!r} is " + "accepted by the schema but not implemented until v0.3.0 " + "step 7 — use 'markov'" + ) + teacher_forcing = _get_path(cfg, "stage2_model.autoregressive.teacher_forcing") + if teacher_forcing != "always": + raise ValueError( + "stage2_model.autoregressive.teacher_forcing = " + f"{teacher_forcing!r} is accepted by the schema but not " + "implemented until v0.3.0 step 7 — use 'always'" + ) + _CONDITIONING_CODE = {"physical": "phys", "embedding": "emb", "onehot": "oh"} diff --git a/giant/model/network.py b/giant/model/network.py index 3a52b32..581e1dd 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -782,6 +782,43 @@ def build_trunk( return MonolithicTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) +# --------------------------------------------------------------------------- +# History encoders — stage-2 autoregressive only (docs/v0.3.0-design.md §6.2) +# --------------------------------------------------------------------------- + + +class HistoryEncoder(nn.Module): + """Interface for stage-2 autoregressive per-token history summaries: + `forward(feat, has_prev) -> (B, K, out_dim)`. `MarkovHistory` is the only + implementation until v0.3.0 step 7 (`AttentionHistory`, + `history = "attention"`).""" + + def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: + raise NotImplementedError + + +class MarkovHistory(HistoryEncoder): + """Summarizes the previous secondary's own `(energy_fraction, direction, + type_representation)` through one small MLP — the "markov" history + (docs/v0.3.0-design.md §6.2): token i+1 only ever sees token i plus the + running scalars (`remaining_frac`/`slot_idx`, fused in separately by + `Stage2Autoregressive._token_cond`), not the full prefix. + + At slot 0 (`has_prev` False) substitutes a learned start vector rather + than zeros — a reasonable default, not specified by the design doc. + """ + + def __init__(self, in_dim: int, out_dim: int) -> None: + super().__init__() + self.start = nn.Parameter(torch.zeros(in_dim)) + self.mlp = nn.Sequential(nn.Linear(in_dim, out_dim), nn.SiLU()) + + def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: + start = self.start.view(1, 1, -1).expand_as(feat) + x = torch.where(has_prev.unsqueeze(-1), feat, start) + return self.mlp(x) + + # --------------------------------------------------------------------------- # Stage models (docs/v0.3.0-design.md §5.3) # --------------------------------------------------------------------------- @@ -1041,16 +1078,214 @@ class Stage2OneShot(nn.Module): class Stage2Autoregressive(nn.Module): - """Not implemented until v0.3.0 steps 4-7 (docs/v0.3.0-design.md §6, §12) - — stub so `stage2_model.decoder = "autoregressive"` (the v0.3.0 default) - fails loudly instead of silently no-op-ing.""" + """Emits secondaries one at a time in descending-energy order + (docs/v0.3.0-design.md §6), instead of `Stage2OneShot`'s simultaneous + k_max-slot prediction. Only `history = "markov"` is implemented (v0.3.0 + step 5) — `history = "attention"` raises immediately at construction. + `teacher_forcing` handling lives entirely in the trainer + (`giant/train.py`), since it only affects how training inputs are + assembled, not this module's architecture. - def __init__(self, *args, **kwargs) -> None: + Under teacher forcing every token's conditioning is built from ground + truth, so a whole K-token sequence trains in one parallel batched pass: + `forward` accepts `(B, K, ...)` tensors for an arbitrary K (not hardcoded + to `k_max`) — this also means a future one-token-at-a-time inference loop + (`K=1` per call, step 6) needs no interface change here. + + Two independent conditioning paths, mirroring `Stage2OneShot`'s + `_cond_embed` but split in two: `_base_cond` (`cond_enc` + + `context_adapter` only) feeds `predict_n_sec`, since n_sec doesn't depend + on token position; `_token_cond` additionally fuses in the history + encoding and two running scalars (remaining energy-budget fraction, + normalized slot index), and feeds `forward`/`predict_type`/the trunk. + """ + + def __init__( + self, + pdg_vocab: int, + mat_vocab: int, + particle_cfg: dict, + material_cfg: dict, + hidden_dim: int = 256, + n_res_blocks: int = 6, + cond_out_dim: int = 128, + context_dim: int = 64, + x_dim: int = X_DIM, + dropout: float = 0.0, + generator: str = "wgan", + time_dim: int = 64, + noise_dim: int = 64, + k_max: int = K_MAX, + router: Router | None = None, + build_n_sec_head: bool = True, + particle_type_cfg: dict | None = None, + history: str = "markov", + ) -> None: super().__init__() - raise NotImplementedError( - "stage2_model.decoder = 'autoregressive' is not implemented yet " - "(design doc v0.3.0 steps 4-7) — use decoder = 'one_shot' for now" + if history != "markov": + raise NotImplementedError( + f"stage2_model.autoregressive.history={history!r} is not " + "implemented until v0.3.0 step 7 — use 'markov'" + ) + self.generator_kind = generator + self.noise_dim = noise_dim + self.k_max = k_max + self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) + emb_dim = particle_cfg["emb_dim"] + self.type_dim = stage2_type_dim(self.particle_type_cfg, emb_dim) + + self.cond_enc = ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim ) + self.context_adapter = ContextAdapter(x_dim, context_dim) + self.base_fuse = nn.Sequential( + nn.Linear(cond_out_dim + context_dim, cond_out_dim), + nn.SiLU(), + ) + + # Reuses conditioning.out_dim for the history encoder's own output + # width — there's no dedicated stage2_model.autoregressive key for + # this, a reasonable default rather than a design-doc-specified value. + history_dim = cond_out_dim + self.history_encoder = MarkovHistory(CONT_SLOT_DIM + self.type_dim, history_dim) + token_fuse_in = ( + cond_out_dim + context_dim + history_dim + 2 + ) # +2: remaining_frac, slot_idx + self.token_fuse = nn.Sequential( + nn.Linear(token_fuse_in, cond_out_dim), + nn.SiLU(), + ) + + has_time = generator in ("flow", "ddpm") + self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None + merged_cond_dim = (time_dim if has_time else 0) + cond_out_dim + token_dim = stage2_trunk_sec_dim(self.particle_type_cfg, generator, 1, emb_dim) + in_dim = noise_dim if generator == "wgan" else token_dim + self.trunk = build_trunk( + router, + in_dim, + token_dim, + hidden_dim, + n_res_blocks, + merged_cond_dim, + dropout, + ) + + self.n_sec_head = None + if build_n_sec_head: + self.n_sec_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, k_max + 1), + ) + self.type_head = None + target = self.particle_type_cfg.get("target", "physical") + if target != "physical" and generator in ("flow", "ddpm"): + self.type_head = nn.Sequential( + nn.Linear(cond_out_dim, hidden_dim // 2), + nn.SiLU(), + nn.Linear(hidden_dim // 2, self.type_dim), + ) + + def _base_cond( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor + ) -> torch.Tensor: + base = self.cond_enc(cond_cont, cond_cat) + ctx = self.context_adapter(stage1_out) + return self.base_fuse(torch.cat([base, ctx], dim=-1)) + + def _token_cond( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + history_feat: torch.Tensor, + has_prev: torch.Tensor, + remaining_frac: torch.Tensor, + slot_idx: torch.Tensor, + ) -> torch.Tensor: + K = history_feat.size(1) + base = self.cond_enc(cond_cont, cond_cat).unsqueeze(1).expand(-1, K, -1) + ctx = self.context_adapter(stage1_out).unsqueeze(1).expand(-1, K, -1) + hist = self.history_encoder(history_feat, has_prev) + scalars = torch.stack([remaining_frac, slot_idx], dim=-1) + return self.token_fuse(torch.cat([base, ctx, hist, scalars], dim=-1)) + + def forward( + self, + x_t: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + history_feat: torch.Tensor, + has_prev: torch.Tensor, + remaining_frac: torch.Tensor, + slot_idx: torch.Tensor, + t: torch.Tensor | None = None, + ) -> torch.Tensor: + B, K = x_t.shape[0], x_t.shape[1] + c_emb = self._token_cond( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ) + if self.time_emb is not None: + assert t is not None + t_emb = self.time_emb(t.reshape(-1)).view(B, K, -1) + cond = torch.cat([t_emb, c_emb], dim=-1) + else: + cond = c_emb + x_flat = x_t.reshape(B * K, -1) + cond_flat = cond.reshape(B * K, -1) + cond_cont_flat = cond_cont.unsqueeze(1).expand(-1, K, -1).reshape(B * K, -1) + cond_cat_flat = cond_cat.unsqueeze(1).expand(-1, K, -1).reshape(B * K, -1) + out = self.trunk(x_flat, cond_flat, cond_cont_flat, cond_cat_flat) + return out.view(B, K, -1) + + def predict_n_sec( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor + ) -> torch.Tensor: + if self.n_sec_head is None: + raise RuntimeError( + "this Stage2Autoregressive has no n_sec_head — it belongs to " + "a migrated v0.2 checkpoint (legacy_owner='stage1'); call " + "stage1.predict_n_sec(cond_cont, cond_cat) instead" + ) + return self.n_sec_head(self._base_cond(cond_cont, cond_cat, stage1_out)) + + def predict_type( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + history_feat: torch.Tensor, + has_prev: torch.Tensor, + remaining_frac: torch.Tensor, + slot_idx: torch.Tensor, + ) -> torch.Tensor: + if self.type_head is None: + raise RuntimeError( + "this Stage2Autoregressive has no type_head — either " + "particle_type.target='physical' (the type slice is part of " + "forward()'s own output) or generator='wgan' (the WGAN " + "trainer reads the type slice out of forward()'s output " + "directly instead)" + ) + c_emb = self._token_cond( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ) + B, K, _ = c_emb.shape + return self.type_head(c_emb.reshape(B * K, -1)).view(B, K, self.type_dim) class CriticModel(nn.Module): @@ -1299,9 +1534,6 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: if s2cfg.get("active", True): decoder = s2cfg.get("decoder", "one_shot") - if decoder == "autoregressive": - result["stage2"] = Stage2Autoregressive() - return result router_cfg = s2cfg.get("router") or {} stage2_router: Router | None = None if router_cfg.get("enabled"): @@ -1316,28 +1548,51 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") k_max = s2cfg.get("k_max", K_MAX) particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} - sec_dim = stage2_trunk_sec_dim( - particle_type_cfg, generator, k_max, particle_cfg["emb_dim"] - ) - result["stage2"] = Stage2OneShot( - pdg_vocab=pdg_vocab, - mat_vocab=mat_vocab, - particle_cfg=particle_cfg, - material_cfg=material_cfg, - hidden_dim=s2cfg.get("hidden_dim", 256), - n_res_blocks=s2cfg.get("n_res_blocks", 6), - cond_out_dim=cond_out_dim, - context_dim=s2cfg.get("context_dim", 64), - sec_dim=sec_dim, - dropout=s2cfg.get("dropout", 0.0), - generator=generator, - time_dim=gen_sub.get("time_dim", 64), - noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), - k_max=k_max, - router=stage2_router, - build_n_sec_head=legacy_owner != "stage1", - particle_type_cfg=particle_type_cfg, - ) + + if decoder == "autoregressive": + ar_cfg = s2cfg.get("autoregressive") or {} + result["stage2"] = Stage2Autoregressive( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=s2cfg.get("hidden_dim", 256), + n_res_blocks=s2cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + context_dim=s2cfg.get("context_dim", 64), + dropout=s2cfg.get("dropout", 0.0), + generator=generator, + time_dim=gen_sub.get("time_dim", 64), + noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), + k_max=k_max, + router=stage2_router, + build_n_sec_head=legacy_owner != "stage1", + particle_type_cfg=particle_type_cfg, + history=ar_cfg.get("history", "markov"), + ) + else: + sec_dim = stage2_trunk_sec_dim( + particle_type_cfg, generator, k_max, particle_cfg["emb_dim"] + ) + result["stage2"] = Stage2OneShot( + pdg_vocab=pdg_vocab, + mat_vocab=mat_vocab, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=s2cfg.get("hidden_dim", 256), + n_res_blocks=s2cfg.get("n_res_blocks", 6), + cond_out_dim=cond_out_dim, + context_dim=s2cfg.get("context_dim", 64), + sec_dim=sec_dim, + dropout=s2cfg.get("dropout", 0.0), + generator=generator, + time_dim=gen_sub.get("time_dim", 64), + noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), + k_max=k_max, + router=stage2_router, + build_n_sec_head=legacy_owner != "stage1", + particle_type_cfg=particle_type_cfg, + ) return result @@ -1377,11 +1632,7 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: stage="stage1", ) - if ( - s2cfg.get("active", True) - and s2cfg.get("generator") == "wgan" - and s2cfg.get("decoder", "one_shot") != "autoregressive" - ): + if s2cfg.get("active", True) and s2cfg.get("generator") == "wgan": k_max = s2cfg.get("k_max", K_MAX) particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} in_dim = stage2_trunk_sec_dim( diff --git a/giant/model/schedule.py b/giant/model/schedule.py index 0a5bf3d..5c1e019 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -132,3 +132,70 @@ def flow_matching_loss_secondary( phys_err = err[..., CONT_SLOT_DIM : CONT_SLOT_DIM + type_dim].mean(dim=-1) phys_loss = (phys_err * mask).sum() / denom return cont_loss + phys_loss + + +def flow_matching_loss_secondary_ar( + model: torch.nn.Module, + x1: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + history_feat: torch.Tensor, + has_prev: torch.Tensor, + remaining_frac: torch.Tensor, + slot_idx: torch.Tensor, + sec_mask: torch.Tensor, + type_dim: int | None = None, +) -> torch.Tensor: + """`Stage2Autoregressive` analogue of `flow_matching_loss_secondary`, same + masked, per-block (continuous vs. type) loss recipe — but native to + `Stage2Autoregressive`'s `(B, K_MAX, token_dim)` I/O and its extra + per-token conditioning args, rather than a flattened `(B, K_MAX*token_dim)` + vector. Kept as a sibling rather than unified with the flat version: the + model call signature differs enough (four extra per-token conditioning + tensors) that merging would need an awkward shape-flag + closure. + + Under teacher forcing (docs/v0.3.0-design.md §6.2 point 3) this is still a + single parallel pass over all K_MAX tokens — `x1`/`history_feat`/etc. are + already built from ground truth for every slot by the caller + (`giant.train._assemble_stage2_ar_inputs`/`_assemble_stage2_ar_target`). + + x1: (B, K_MAX, CONT_SLOT_DIM + type_dim) — per-token flattened target + (stick_logit, dir, then a `type_dim`-wide type slice) + sec_mask: (B, K_MAX) bool — True for valid secondary slots + type_dim: as `flow_matching_loss_secondary` — defaults to + `PARTICLE_PHYS_DIM`, `0` means no type slice is in `x1` at all. + """ + from giant.constants import CONT_SLOT_DIM, PARTICLE_PHYS_DIM + + if type_dim is None: + type_dim = PARTICLE_PHYS_DIM + + B, K, _ = x1.shape + t = torch.rand(B, K, device=x1.device) + x0 = torch.randn_like(x1) + x_t = (1.0 - t.unsqueeze(-1)) * x0 + t.unsqueeze(-1) * x1 + u_t = x1 - x0 + v_t = model( + x_t, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + t=t, + ) + + err = (v_t - u_t) ** 2 + cont_err = err[..., :CONT_SLOT_DIM].mean(dim=-1) # (B, K_MAX) + + mask = sec_mask.float() + denom = mask.sum().clamp(min=1) + cont_loss = (cont_err * mask).sum() / denom + if type_dim == 0: + return cont_loss + phys_err = err[..., CONT_SLOT_DIM : CONT_SLOT_DIM + type_dim].mean(dim=-1) + phys_loss = (phys_err * mask).sum() / denom + return cont_loss + phys_loss diff --git a/giant/train.py b/giant/train.py index 53403c9..a117d2f 100644 --- a/giant/train.py +++ b/giant/train.py @@ -16,7 +16,7 @@ import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm -from giant.constants import CONT_SLOT_DIM, K_MAX +from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM from giant.data.loader import TopNMap from giant.data.setup_cache import topnmap_to_json from giant.model.network import Router, stage2_type_dim @@ -24,6 +24,7 @@ from giant.model.schedule import ( CosineSchedule, flow_matching_loss, flow_matching_loss_secondary, + flow_matching_loss_secondary_ar, ) from giant.model.wgan import gradient_penalty, generator_loss from giant.validate import validate_marginals @@ -102,6 +103,67 @@ def _batch_to_device(batch: tuple, device: torch.device) -> tuple: return tuple(t.to(device) for t in batch) +def _type_repr( + sec_type_idx: torch.Tensor, + sec_cont: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """(B, K_MAX, type_dim) ground-truth type representation, generator- + independent (unlike `_assemble_stage2_ar_target`'s training *target*, + which varies by generator/objective — see its docstring): `"physical"` -> + `(log_mass, charge)`; `"onehot"` -> one-hot of the true class; + `"embedding"` -> the conditioning's own detached embedding-table row. + + Used both to build `_assemble_stage2_ar_target`'s wgan+onehot/embedding + branch and as the AR history features' previous-secondary identity — the + latter must always reflect the true physical secondary that came before, + regardless of what the *current* token's own training objective is. + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM] + if target == "onehot": + return F.one_hot(sec_type_idx, num_classes=emb_dim).float() + return cond_enc.pdg_emb(sec_type_idx).detach() + + +def _assemble_stage2_ar_target( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + generator: str, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """(B, K_MAX, token_dim) ground-truth per-token target — the unflattened + analogue of `_assemble_stage2_real` (defined below in terms of this), + matching whatever width `Stage2Autoregressive`'s (or `Stage2OneShot`'s) + own trunk produces for this (target, generator) combination + (`giant.model.network.stage2_trunk_sec_dim`; docs/v0.3.0-design.md + decision 2/3): + + - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, + dir, log_mass, charge) as-is. + - `target` in `("onehot", "embedding")` + `generator in ("flow", "ddpm")`: + just the continuous stick/dir slots — the type slice isn't part of + this tensor at all (`type_head` handles it separately). + - `target` in `("onehot", "embedding")` + `generator == "wgan"`: stick/dir + slots concatenated with the per-slot type representation (a one-hot of + the true class, relaxed on the *generated* side only, by the caller; + or the conditioning's own detached embedding-table row). + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return sec_cont + cont = sec_cont[..., :CONT_SLOT_DIM] + if generator != "wgan": + return cont + type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) + return torch.cat([cont, type_repr], dim=-1) + + def _assemble_stage2_real( sec_cont: torch.Tensor, sec_type_idx: torch.Tensor, @@ -110,38 +172,87 @@ def _assemble_stage2_real( cond_enc: torch.nn.Module, emb_dim: int, ) -> torch.Tensor: - """Ground-truth flattened stage-2 vector, matching whatever width - `Stage2OneShot`'s own trunk produces for this (target, generator) - combination (`giant.model.network.stage2_trunk_sec_dim`; - docs/v0.3.0-design.md decision 2/3): + """Ground-truth flattened stage-2 vector for `Stage2OneShot` — the + flattened form of `_assemble_stage2_ar_target`, which + `Stage2Autoregressive`'s per-token target also uses; the two must stay in + lockstep. See `_assemble_stage2_ar_target`'s docstring for the + (target, generator) width rules.""" + return _assemble_stage2_ar_target( + sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim + ).flatten(1) - - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, - dir, log_mass, charge) flattened as-is. - - `target` in `("onehot", "embedding")` + `generator in ("flow", "ddpm")`: - just the continuous stick/dir slots — the type slice isn't part of - this vector at all (`Stage2OneShot.type_head` handles it separately). - - `target` in `("onehot", "embedding")` + `generator == "wgan"`: stick/dir - slots concatenated with the per-slot type vector — a one-hot of the - true class (`"onehot"`, relaxed on the *generated* side only, by the - caller) or the conditioning's own detached embedding-table row - (`"embedding"`, already continuous — no relaxation needed either side, - §2.1). - """ - target = particle_type_cfg.get("target", "physical") - if target == "physical": - return sec_cont.flatten(1) - cont = sec_cont[..., :CONT_SLOT_DIM] - if generator != "wgan": - return cont.flatten(1) - if target == "onehot": - type_vec = F.one_hot(sec_type_idx, num_classes=emb_dim).float() - else: - type_vec = cond_enc.pdg_emb(sec_type_idx).detach() - return torch.cat([cont, type_vec], dim=-1).flatten(1) + +def _stick_fraction(sec_cont: torch.Tensor) -> torch.Tensor: + """(B, K_MAX) — sigmoid of each slot's own stick-breaking logit + (`sec_cont[...,0]`); scale-free (see `giant.data.transforms. + encode_secondaries`), so this needs no absolute `e_sec`.""" + return torch.sigmoid(sec_cont[..., 0]) + + +def _remaining_energy_fraction(fraction: torch.Tensor) -> torch.Tensor: + """(B, K_MAX) — fraction of the original e_sec budget unclaimed entering + slot i: `1.0` at `i=0`, `prod_{j=1` + (docs/v0.3.0-design.md §6.3 — "no re-derivation needed": the existing + stick-breaking encoding is already scale-free, so this is derivable from + the batch's ground-truth stick logits alone, no `e_sec` required).""" + cumprod = torch.cumprod(1.0 - fraction, dim=1) + return torch.cat([torch.ones_like(cumprod[:, :1]), cumprod[:, :-1]], dim=1) + + +def _shift_prev(x: torch.Tensor) -> torch.Tensor: + """`(B, K, ...)` -> same shape, slot i holds slot i-1's value; slot 0 gets + an arbitrary zero placeholder (never read as-is — see `_ar_has_prev`; + `MarkovHistory` substitutes its own learned start vector there instead).""" + return torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + + +def _ar_has_prev(k_max: int, device: torch.device) -> torch.Tensor: + """`(1, K_MAX)` bool: True for slot index `>= 1`. Correct without + `n_sec`: `sec_mask` is a prefix mask, so any *valid* token at `k>=1` + always has a valid predecessor at `k-1`; the only wrong cases are tokens + that are themselves padding, already masked out of every loss.""" + return (torch.arange(k_max, device=device) >= 1).unsqueeze(0) + + +def _assemble_stage2_ar_inputs( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> dict[str, torch.Tensor]: + """Ground-truth per-token AR conditioning tensors — all `(B, K_MAX, ...)` + or `(B, K_MAX)`, built in one vectorized pass (teacher forcing means + every token's input is ground truth, docs/v0.3.0-design.md §6.2 point 3). + Keys match `Stage2Autoregressive.forward`'s trailing kwargs.""" + device = sec_cont.device + B, K = sec_cont.shape[0], sec_cont.shape[1] + fraction = _stick_fraction(sec_cont) + type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) + history_feat = torch.cat( + [ + _shift_prev(fraction).unsqueeze(-1), + _shift_prev(sec_cont[..., 1:CONT_SLOT_DIM]), + _shift_prev(type_repr), + ], + dim=-1, + ) + slot_idx = (torch.arange(K, device=device).float() / max(K - 1, 1)).unsqueeze(0) + return { + "history_feat": history_feat, + "has_prev": _ar_has_prev(K, device).expand(B, -1), + "remaining_frac": _remaining_energy_fraction(fraction), + "slot_idx": slot_idx.expand(B, -1), + } def _relax_onehot_type_slice( - x_flat: torch.Tensor, k_max: int, cont_dim: int, type_dim: int, tau: float + x_flat: torch.Tensor, + k_max: int, + cont_dim: int, + type_dim: int, + tau: float, + grad_probe: dict[str, float] | None = None, ) -> torch.Tensor: """Straight-through Gumbel-softmax relaxation of the per-slot type slice inside a flattened `(B, k_max * (cont_dim + type_dim))` WGAN generator @@ -149,10 +260,26 @@ def _relax_onehot_type_slice( hard one-hot (matching what the critic sees from real data), the backward pass flows smooth gradient. Continuous slots (stick/dir, and the type slice itself under `target = "embedding"`, which never calls - this) pass through unchanged.""" + this) pass through unchanged. + + `grad_probe`, if given, gets `["cont"]`/`["type"]` populated with the L2 + norm of the gradient reaching this split point during the next + `.backward()` call that touches it — a backward hook, not a second + backward pass. This is the §11.4 differentiability validation-obligation + instrumentation (docs/v0.3.0-design.md): the trunk-gradient contribution + from the type slice vs. the continuous slices, for + `particle_type.target="onehot"` + `generator="wgan"`. Only ever populated + on a `did_g_step` batch — the critic step backprops through + `fake.detach()`, which never reaches these hooks — so it stays empty + (callers default to `0.0`) otherwise.""" B = x_flat.size(0) x = x_flat.view(B, k_max, cont_dim + type_dim) cont, type_logits = x[..., :cont_dim], x[..., cont_dim:] + if grad_probe is not None: + cont.register_hook(lambda g: grad_probe.__setitem__("cont", g.norm().item())) + type_logits.register_hook( + lambda g: grad_probe.__setitem__("type", g.norm().item()) + ) type_soft = F.gumbel_softmax(type_logits, tau=tau, hard=True, dim=-1) return torch.cat([cont, type_soft], dim=-1).reshape(B, -1) @@ -234,6 +361,7 @@ class FlowDDPMStageTrainer(StageTrainer): device: torch.device, particle_type_cfg: dict | None = None, particle_type_emb_dim: int = 16, + decoder: str = "one_shot", ) -> None: if is_stage2 and generator not in ("flow",): raise NotImplementedError( @@ -244,6 +372,7 @@ class FlowDDPMStageTrainer(StageTrainer): self.name = name self.is_stage2 = is_stage2 self.generator = generator + self.decoder = decoder self.device = device self.model = model.to(device) self.lambda_weight = lambda_weight @@ -302,12 +431,29 @@ class FlowDDPMStageTrainer(StageTrainer): return self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) return self.model.predict_n_sec(cond_cont, cond_cat) - def _generator_loss(self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx): + def _generator_loss( + self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None + ): if not self.is_stage2: if self.generator == "flow": return flow_matching_loss(self.model, x1_s1, cond_cont, cond_cat) assert self.ddpm_schedule is not None return self.ddpm_schedule.loss(self.model, x1_s1, cond_cont, cond_cat) + if self.decoder == "autoregressive": + assert ar_inputs is not None + return flow_matching_loss_secondary_ar( + self.model, + x1_s2, + cond_cont, + cond_cat, + stage1_ctx, + ar_inputs["history_feat"], + ar_inputs["has_prev"], + ar_inputs["remaining_frac"], + ar_inputs["slot_idx"], + sec_mask, + type_dim=self._flow_type_dim, + ) return flow_matching_loss_secondary( self.model, x1_s2, @@ -319,10 +465,17 @@ class FlowDDPMStageTrainer(StageTrainer): ) def _type_loss( - self, cond_cont, cond_cat, stage1_ctx, sec_type_idx, sec_mask, device + self, + cond_cont, + cond_cat, + stage1_ctx, + sec_type_idx, + sec_mask, + device, + ar_inputs=None, ): - """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for - `Stage2OneShot.type_head` — the non-adversarial counterpart to + """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for the + stage-2 model's `type_head` — the non-adversarial counterpart to WGANStageTrainer's ST-Gumbel-into-the-critic path (decision 2/5). Zero when this stage has no `type_head` (stage 1, or `particle_type.target = "physical"`).""" @@ -331,7 +484,19 @@ class FlowDDPMStageTrainer(StageTrainer): type_head = getattr(self.model, "type_head", None) if not self.is_stage2 or type_head is None: return l_type, type_acc - type_out = self.model.predict_type(cond_cont, cond_cat, stage1_ctx) + if self.decoder == "autoregressive": + assert ar_inputs is not None + type_out = self.model.predict_type( + cond_cont, + cond_cat, + stage1_ctx, + ar_inputs["history_feat"], + ar_inputs["has_prev"], + ar_inputs["remaining_frac"], + ar_inputs["slot_idx"], + ) + else: + type_out = self.model.predict_type(cond_cont, cond_cat, stage1_ctx) mask = sec_mask.float() denom = mask.sum().clamp(min=1) if self.particle_type_cfg.get("target") == "onehot": @@ -360,8 +525,27 @@ class FlowDDPMStageTrainer(StageTrainer): ) = _batch_to_device(batch, device) sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) stage1_ctx = x1_s1.detach() - x1_s2 = ( - _assemble_stage2_real( + + x1_s2 = None + ar_inputs = None + if self.is_stage2 and self.decoder == "autoregressive": + ar_inputs = _assemble_stage2_ar_inputs( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + x1_s2 = _assemble_stage2_ar_target( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.generator, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + elif self.is_stage2: + x1_s2 = _assemble_stage2_real( sec_cont, sec_type_idx, self.particle_type_cfg, @@ -369,12 +553,9 @@ class FlowDDPMStageTrainer(StageTrainer): self.model.cond_enc, self.particle_type_emb_dim, ) - if self.is_stage2 - else None - ) l_gen = self._generator_loss( - cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx + cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs ) n_sec_logits = self._predict_n_sec(cond_cont, cond_cat, stage1_ctx) l_nsec = torch.zeros((), device=device) @@ -384,7 +565,13 @@ class FlowDDPMStageTrainer(StageTrainer): nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() l_type, type_acc = self._type_loss( - cond_cont, cond_cat, stage1_ctx, sec_type_idx, sec_mask, device + cond_cont, + cond_cat, + stage1_ctx, + sec_type_idx, + sec_mask, + device, + ar_inputs=ar_inputs, ) l_balance = l_proc = l_entropy = torch.zeros((), device=device) @@ -531,9 +718,11 @@ class WGANStageTrainer(StageTrainer): particle_type_emb_dim: int = 16, type_gumbel_tau_start: float = 1.0, type_gumbel_tau_end: float = 0.1, + decoder: str = "one_shot", ) -> None: self.name = name self.is_stage2 = is_stage2 + self.decoder = decoder self.device = device self.model = model.to(device) self.critic = critic.to(device) @@ -592,6 +781,7 @@ class WGANStageTrainer(StageTrainer): ) = _batch_to_device(batch, device) B = cond_cont.size(0) stage1_ctx = x1_s1.detach() + grad_probe: dict[str, float] = {} if not self.is_stage2: real = x1_s1 @@ -615,28 +805,62 @@ class WGANStageTrainer(StageTrainer): mask = ( sec_mask.unsqueeze(-1).expand(-1, -1, slot_width).reshape(B, -1).float() ) - real = ( - _assemble_stage2_real( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - "wgan", - self.model.cond_enc, - self.particle_type_emb_dim, - ) - * mask - ) def critic_fn(x): return self.critic(x, cond_cont, cond_cat, stage1_ctx) - z = torch.randn(B, self.model.noise_dim, device=device) - fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) + if self.decoder == "autoregressive": + ar = _assemble_stage2_ar_inputs( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + real = ( + _assemble_stage2_ar_target( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + "wgan", + self.model.cond_enc, + self.particle_type_emb_dim, + ).reshape(B, -1) + * mask + ) + z = torch.randn(B, K_MAX, self.model.noise_dim, device=device) + fake_raw = self.model( + z, + cond_cont, + cond_cat, + stage1_ctx, + ar["history_feat"], + ar["has_prev"], + ar["remaining_frac"], + ar["slot_idx"], + ).reshape(B, -1) + else: + real = ( + _assemble_stage2_real( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + "wgan", + self.model.cond_enc, + self.particle_type_emb_dim, + ) + * mask + ) + z = torch.randn(B, self.model.noise_dim, device=device) + fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) + if target == "onehot": # Straight-through Gumbel-softmax relaxation of the type # slice only (decision 5) — the critic must see a hard # one-hot forward (matching what "real" data looks like) # while gradient still flows smoothly to the generator. + # grad_probe captures the §11.4 gradient-magnitude + # instrumentation — see _relax_onehot_type_slice's docstring. tau = _gumbel_tau( global_step, self.total_steps, @@ -644,7 +868,7 @@ class WGANStageTrainer(StageTrainer): self.type_gumbel_tau_end, ) fake_raw = _relax_onehot_type_slice( - fake_raw, K_MAX, CONT_SLOT_DIM, type_dim, tau + fake_raw, K_MAX, CONT_SLOT_DIM, type_dim, tau, grad_probe=grad_probe ) fake = fake_raw * mask @@ -711,6 +935,8 @@ class WGANStageTrainer(StageTrainer): "grad_norm": grad_norm_d.item() + grad_norm_g.item(), "grad_norm_d": grad_norm_d.item(), "grad_norm_g": grad_norm_g.item(), + "grad_norm_type_slice": grad_probe.get("type", 0.0), + "grad_norm_cont_slice": grad_probe.get("cont", 0.0), "lr": self.optimizer.param_groups[0]["lr"], "critic_lr": self.optimizer_d.param_groups[0]["lr"], "batch_size": B, @@ -820,6 +1046,17 @@ def _build_stage_trainers( "target": "physical" } particle_type_emb_dim = cfg["conditioning"]["particle"]["emb_dim"] + decoder = stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot" + if is_stage2 and decoder == "autoregressive": + teacher_forcing = (stage_cfg.get("autoregressive") or {}).get( + "teacher_forcing", "always" + ) + if teacher_forcing != "always": + raise NotImplementedError( + "stage2_model.autoregressive.teacher_forcing=" + f"{teacher_forcing!r} is not implemented until v0.3.0 " + "step 7 — use 'always'" + ) if generator == "wgan": critic = critics.get(name) @@ -848,6 +1085,7 @@ def _build_stage_trainers( particle_type_emb_dim=particle_type_emb_dim, type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), + decoder=decoder, ) else: ddpm_n_steps = stage_cfg.get("ddpm", {}).get("n_steps", 1000) @@ -873,6 +1111,7 @@ def _build_stage_trainers( device=device, particle_type_cfg=particle_type_cfg, particle_type_emb_dim=particle_type_emb_dim, + decoder=decoder, ) return trainers @@ -892,6 +1131,14 @@ def _metrics_fields(trainers: dict[str, StageTrainer]) -> list[str]: f"{name}_train_grad_norm_d", f"{name}_train_grad_norm_g", ] + if ( + trainer.is_stage2 + and trainer.particle_type_cfg.get("target") == "onehot" + ): + fields += [ + f"{name}_train_grad_norm_type_slice", + f"{name}_train_grad_norm_cont_slice", + ] else: fields += [ f"{name}_train_loss", @@ -1358,6 +1605,13 @@ def train( metrics_row[f"{name}_train_grad_norm_g"] = ( sums.get("grad_norm_g", 0.0) / n_train ) + if tr.is_stage2 and tr.particle_type_cfg.get("target") == "onehot": + metrics_row[f"{name}_train_grad_norm_type_slice"] = ( + sums.get("grad_norm_type_slice", 0.0) / n_train + ) + metrics_row[f"{name}_train_grad_norm_cont_slice"] = ( + sums.get("grad_norm_cont_slice", 0.0) / n_train + ) grad_norm_total += sums.get("grad_norm", 0.0) / n_train metrics_row[f"{name}_critic_lr"] = tr.optimizer_d.param_groups[0][ "lr" diff --git a/tests/test_config.py b/tests/test_config.py index 653fca1..4fb9b00 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -637,6 +637,55 @@ def test_validate_config_stop_token_not_implemented(): assert "stop_token" in str(e) +def test_validate_config_ar_default_markov_always_passes(): + """DEFAULT_CONFIG already has decoder='autoregressive', + history='markov', teacher_forcing='always' — must not raise (v0.3.0 + step 5; see also test_validate_config_default_config_passes).""" + cfg = _cfg_with(**{"stage2_model.decoder": "autoregressive"}) + gconfig.validate_config(cfg) # must not raise + + +def test_validate_config_ar_history_attention_not_implemented(): + cfg = _cfg_with( + **{ + "stage2_model.decoder": "autoregressive", + "stage2_model.autoregressive.history": "attention", + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "history" in str(e) + + +def test_validate_config_ar_teacher_forcing_scheduled_not_implemented(): + cfg = _cfg_with( + **{ + "stage2_model.decoder": "autoregressive", + "stage2_model.autoregressive.teacher_forcing": "scheduled", + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "teacher_forcing" in str(e) + + +def test_validate_config_ar_checks_skipped_under_one_shot(): + """history/teacher_forcing values that would fail under AR are irrelevant + (and unchecked) when decoder='one_shot'.""" + cfg = _cfg_with( + **{ + "stage2_model.decoder": "one_shot", + "stage2_model.autoregressive.history": "attention", + "stage2_model.autoregressive.teacher_forcing": "scheduled", + } + ) + gconfig.validate_config(cfg) # must not raise + + # --------------------------------------------------------------------------- # checkpoint config-mismatch warnings (unchanged surface, still exercised) # --------------------------------------------------------------------------- diff --git a/tests/test_network.py b/tests/test_network.py index 2117898..dde4bf9 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -1,9 +1,12 @@ +import pytest import torch from giant.constants import CONT_SLOT_DIM, COND_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM from giant.model.network import ( ConditionEncoder, + MarkovHistory, SinusoidalEmbedding, Stage1Model, + Stage2Autoregressive, Stage2OneShot, cat_col_layout, stage2_trunk_sec_dim, @@ -290,3 +293,228 @@ def test_stage2_oneshot_forward_shape_onehot_flow_excludes_type(): t = torch.rand(B) out = model(x_t, cond_cont, cond_cat, stage1_out, t=t) assert out.shape == (B, k_max * CONT_SLOT_DIM) + + +# --- MarkovHistory (docs/v0.3.0-design.md §6.2) ----------------------------- + + +def test_markov_history_shape(): + hist = MarkovHistory(in_dim=7, out_dim=12) + B, K = 3, 5 + feat = torch.randn(B, K, 7) + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + out = hist(feat, has_prev) + assert out.shape == (B, K, 12) + + +def test_markov_history_uses_start_vector_when_no_prev(): + """Slot 0's own raw feature must be ignored — a learned start vector is + substituted there instead (a reasonable default not specified by the + design doc, see Stage2Autoregressive's docstring).""" + hist = MarkovHistory(in_dim=4, out_dim=6) + B, K = 2, 3 + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + feat_a = torch.randn(B, K, 4) + feat_b = feat_a.clone() + feat_b[:, 0] = torch.randn(B, 4) * 100 + out_a = hist(feat_a, has_prev) + out_b = hist(feat_b, has_prev) + assert torch.allclose(out_a[:, 0], out_b[:, 0]) + assert torch.allclose(out_a[:, 1:], out_b[:, 1:]) + + +# --- Stage2Autoregressive (docs/v0.3.0-design.md §6, v0.3.0 step 5) --------- + + +def _build_stage2_ar( + target: str, + generator: str, + emb_dim: int = 6, + k_max: int = 5, + history: str = "markov", +) -> Stage2Autoregressive: + particle_cfg = {"type": "physical", "emb_dim": emb_dim, "n_layers": 1} + if target == "embedding": + particle_cfg = dict(particle_cfg) + particle_cfg["type"] = "embedding" + return Stage2Autoregressive( + pdg_vocab=5, + mat_vocab=3, + particle_cfg=particle_cfg, + material_cfg=MATERIAL_CFG, + hidden_dim=16, + n_res_blocks=1, + cond_out_dim=16, + context_dim=8, + generator=generator, + k_max=k_max, + particle_type_cfg={"target": target, "lambda": 1.0}, + history=history, + ) + + +def _ar_inputs(B: int, K: int, hist_dim: int): + history_feat = torch.randn(B, K, hist_dim) + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + remaining_frac = torch.rand(B, K) + slot_idx = torch.linspace(0, 1, K).unsqueeze(0).expand(B, -1) + return history_feat, has_prev, remaining_frac, slot_idx + + +def test_stage2_autoregressive_history_attention_raises(): + with pytest.raises(NotImplementedError): + _build_stage2_ar("onehot", "wgan", history="attention") + + +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +@pytest.mark.parametrize("generator", ["wgan", "flow"]) +def test_stage2_autoregressive_forward_shape(target, generator): + B, K, emb_dim = 4, 5, 6 + model = _build_stage2_ar(target, generator, emb_dim=emb_dim, k_max=K) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + type_dim = stage2_type_dim({"target": target}, emb_dim) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( + B, K, CONT_SLOT_DIM + type_dim + ) + token_dim = stage2_trunk_sec_dim({"target": target}, generator, 1, emb_dim) + if generator == "wgan": + x_t = torch.randn(B, K, model.noise_dim) + t = None + else: + x_t = torch.randn(B, K, token_dim) + t = torch.rand(B, K) + out = model( + x_t, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + t=t, + ) + assert out.shape == (B, K, token_dim) + + +def test_stage2_autoregressive_predict_n_sec_shape(): + B, k_max, emb_dim = 4, 5, 6 + model = _build_stage2_ar("physical", "wgan", emb_dim=emb_dim, k_max=k_max) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + logits = model.predict_n_sec(cond_cont, cond_cat, stage1_out) + assert logits.shape == (B, k_max + 1) + + +def test_stage2_autoregressive_predict_type_shape(): + B, K, emb_dim = 4, 5, 6 + model = _build_stage2_ar("onehot", "flow", emb_dim=emb_dim, k_max=K) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( + B, K, CONT_SLOT_DIM + type_dim + ) + out = model.predict_type( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ) + assert out.shape == (B, K, emb_dim) + + +@pytest.mark.parametrize("target,generator", [("physical", "flow"), ("onehot", "wgan")]) +def test_stage2_autoregressive_predict_type_raises_when_no_type_head(target, generator): + B, K, emb_dim = 2, 5, 6 + model = _build_stage2_ar(target, generator, emb_dim=emb_dim, k_max=K) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + type_dim = stage2_type_dim({"target": target}, emb_dim) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( + B, K, CONT_SLOT_DIM + type_dim + ) + with pytest.raises(RuntimeError): + model.predict_type( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ) + + +def test_stage2_autoregressive_gradients_flow_wgan_onehot(): + B, K, emb_dim = 4, 5, 6 + model = _build_stage2_ar("onehot", "wgan", emb_dim=emb_dim, k_max=K) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( + B, K, CONT_SLOT_DIM + type_dim + ) + z = torch.randn(B, K, model.noise_dim) + gen_out = model( + z, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ).sum() + nsec_out = model.predict_n_sec(cond_cont, cond_cat, stage1_out).sum() + (gen_out + nsec_out).backward() + for name, p in model.named_parameters(): + assert p.grad is not None, f"no grad for {name}" + + +def test_stage2_autoregressive_gradients_flow_onehot(): + B, K, emb_dim = 4, 5, 6 + model = _build_stage2_ar("onehot", "flow", emb_dim=emb_dim, k_max=K) + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.zeros(B, 2, dtype=torch.long) + stage1_out = torch.randn(B, 9) + type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( + B, K, CONT_SLOT_DIM + type_dim + ) + token_dim = stage2_trunk_sec_dim({"target": "onehot"}, "flow", 1, emb_dim) + x_t = torch.randn(B, K, token_dim) + t = torch.rand(B, K) + flow_out = model( + x_t, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + t=t, + ).sum() + nsec_out = model.predict_n_sec(cond_cont, cond_cat, stage1_out).sum() + type_out = model.predict_type( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ).sum() + (flow_out + nsec_out + type_out).backward() + for name, p in model.named_parameters(): + assert p.grad is not None, f"no grad for {name}" diff --git a/tests/test_phase2.py b/tests/test_phase2.py index deeb9c7..f548f7a 100644 --- a/tests/test_phase2.py +++ b/tests/test_phase2.py @@ -4,9 +4,19 @@ import numpy as np import pytest import torch -from giant.constants import COND_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_DIM, X_DIM -from giant.model.network import Stage1Model, Stage2OneShot -from giant.model.schedule import flow_matching_loss_secondary +from giant.constants import ( + COND_DIM, + CONT_SLOT_DIM, + K_MAX, + PARTICLE_PHYS_DIM, + SEC_DIM, + X_DIM, +) +from giant.model.network import Stage1Model, Stage2Autoregressive, Stage2OneShot +from giant.model.schedule import ( + flow_matching_loss_secondary, + flow_matching_loss_secondary_ar, +) from giant.sample import sample_secondaries _SAMPLE_SECONDARIES_XFAIL_REASON = ( @@ -185,6 +195,108 @@ def test_flow_matching_loss_secondary_has_grad(): assert any(p.grad is not None for p in decoder.parameters()) +# ── masked flow matching loss — autoregressive (v0.3.0 step 5) ───────────── + + +def _sec_decoder_ar(pdg=3, mat=2, k_max=K_MAX): + particle_cfg, material_cfg = _particle_material_cfg("embedding") + return Stage2Autoregressive( + pdg_vocab=pdg, + mat_vocab=mat, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator="flow", + time_dim=16, + k_max=k_max, + ) + + +def _ar_history_inputs(B, K, hist_dim): + history_feat = torch.randn(B, K, hist_dim) + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + remaining_frac = torch.rand(B, K) + slot_idx = torch.linspace(0, 1, K).unsqueeze(0).expand(B, -1) + return history_feat, has_prev, remaining_frac, slot_idx + + +def test_flow_matching_loss_secondary_ar_scalar(): + B, K, pdg, mat = 8, K_MAX, 3, 2 + decoder = _sec_decoder_ar(pdg, mat, k_max=K) + x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) + cond_cont, cond_cat = _cond(B, pdg, mat) + stage1_out = torch.randn(B, X_DIM) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( + B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM + ) + sec_mask = torch.ones(B, K, dtype=torch.bool) + loss = flow_matching_loss_secondary_ar( + decoder, + x1, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + sec_mask, + ) + assert loss.shape == () + assert loss.item() >= 0.0 + + +def test_flow_matching_loss_secondary_ar_mask_zeros_padding(): + B, K, pdg, mat = 4, K_MAX, 3, 2 + decoder = _sec_decoder_ar(pdg, mat, k_max=K) + x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) + cond_cont, cond_cat = _cond(B, pdg, mat) + stage1_out = torch.randn(B, X_DIM) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( + B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM + ) + sec_mask = torch.zeros(B, K, dtype=torch.bool) + loss = flow_matching_loss_secondary_ar( + decoder, + x1, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + sec_mask, + ) + assert loss.item() == pytest.approx(0.0, abs=1e-6) + + +def test_flow_matching_loss_secondary_ar_has_grad(): + B, K, pdg, mat = 4, K_MAX, 3, 2 + decoder = _sec_decoder_ar(pdg, mat, k_max=K) + x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) + cond_cont, cond_cat = _cond(B, pdg, mat) + stage1_out = torch.randn(B, X_DIM) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( + B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM + ) + sec_mask = torch.ones(B, K, dtype=torch.bool) + flow_matching_loss_secondary_ar( + decoder, + x1, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + sec_mask, + ).backward() + assert any(p.grad is not None for p in decoder.parameters()) + + # ── sampling ────────────────────────────────────────────────────────────────── diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index f49c38d..f4c093c 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -105,10 +105,10 @@ def _tiny_cfg(**train_overrides): cfg["train"].update(train_overrides) cfg["stage1_model"].update({"hidden_dim": 8, "n_res_blocks": 1, "dropout": 0.0}) cfg["stage2_model"].update( - # decoder="autoregressive" is DEFAULT_CONFIG's default (the finished - # v0.3.0 target) but Stage2Autoregressive isn't implemented until - # design doc step 4/5 — every run must override to "one_shot" for now. - {"decoder": "one_shot", "hidden_dim": 8, "n_res_blocks": 1, "dropout": 0.0} + # decoder="autoregressive" is DEFAULT_CONFIG's default (v0.3.0 step 5) + # and left as-is here on purpose, so this pipeline-level fixture + # exercises the real default end-to-end against actual data. + {"hidden_dim": 8, "n_res_blocks": 1, "dropout": 0.0} ) cfg["conditioning"]["particle"]["emb_dim"] = 4 cfg["conditioning"]["material"]["emb_dim"] = 4 diff --git a/tests/test_train.py b/tests/test_train.py index e20223d..8318add 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -1,18 +1,36 @@ """Tests for giant/train.py.""" import copy +import csv import tempfile from pathlib import Path import pytest import torch -from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM +from giant.constants import ( + COND_DIM, + CONT_SLOT_DIM, + K_MAX, + PARTICLE_PHYS_DIM, + SEC_SLOT_DIM, + X_DIM, +) from giant.model.network import build_critics, build_models from giant.train import ( FlowDDPMStageTrainer, WGANStageTrainer, + _ar_has_prev, + _assemble_stage2_ar_inputs, + _assemble_stage2_ar_target, + _assemble_stage2_real, + _build_stage_trainers, _gumbel_tau, + _relax_onehot_type_slice, + _remaining_energy_fraction, + _shift_prev, + _stick_fraction, + _type_repr, _wandb_run_config, train, ) @@ -67,6 +85,122 @@ def test_wandb_run_config_handles_missing_model_config(): assert wcfg["model_config"] == {} +# --- AR helper functions (v0.3.0 step 5, docs/v0.3.0-design.md §6) --------- + + +def test_stick_fraction_matches_sigmoid_of_logit(): + sec_cont = torch.zeros(2, 3, SEC_SLOT_DIM) + sec_cont[..., 0] = torch.tensor([[0.0, 2.0, -2.0], [1.0, -1.0, 0.0]]) + frac = _stick_fraction(sec_cont) + assert torch.allclose(frac, torch.sigmoid(sec_cont[..., 0])) + + +def test_remaining_energy_fraction_hand_computed(): + fraction = torch.tensor([[0.5, 0.5, 1.0]]) + remaining = _remaining_energy_fraction(fraction) + assert torch.allclose(remaining, torch.tensor([[1.0, 0.5, 0.25]])) + + +def test_shift_prev_shifts_and_zero_pads_slot0(): + x = torch.arange(2 * 4 * 3).reshape(2, 4, 3).float() + shifted = _shift_prev(x) + assert torch.all(shifted[:, 0] == 0) + assert torch.equal(shifted[:, 1:], x[:, :-1]) + + +def test_ar_has_prev_false_only_at_slot_zero(): + has_prev = _ar_has_prev(5, torch.device("cpu")) + assert has_prev.shape == (1, 5) + assert has_prev.tolist() == [[False, True, True, True, True]] + + +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +def test_type_repr_shapes_and_values(target): + B, K, emb_dim = 3, 4, 6 + sec_cont = torch.randn(B, K, SEC_SLOT_DIM) + sec_type_idx = torch.randint(0, emb_dim, (B, K)) + cond_enc = torch.nn.Module() + if target == "embedding": + cond_enc.pdg_emb = torch.nn.Embedding(emb_dim, emb_dim) + repr_ = _type_repr(sec_type_idx, sec_cont, {"target": target}, cond_enc, emb_dim) + expected_width = PARTICLE_PHYS_DIM if target == "physical" else emb_dim + assert repr_.shape == (B, K, expected_width) + if target == "physical": + assert torch.equal( + repr_, sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM] + ) + if target == "onehot": + assert torch.all(repr_.sum(-1) == 1.0) + + +@pytest.mark.parametrize( + "target,generator", + [ + ("physical", "flow"), + ("physical", "wgan"), + ("onehot", "flow"), + ("onehot", "wgan"), + ("embedding", "flow"), + ("embedding", "wgan"), + ], +) +def test_assemble_stage2_ar_target_matches_assemble_stage2_real_flattened( + target, generator +): + """Regression test tying the refactor together: _assemble_stage2_real is + now defined as _assemble_stage2_ar_target(...).flatten(1).""" + B, emb_dim = 4, 6 + sec_cont = torch.randn(B, K_MAX, SEC_SLOT_DIM) + sec_type_idx = torch.randint(0, emb_dim, (B, K_MAX)) + cond_enc = torch.nn.Module() + if target == "embedding": + cond_enc.pdg_emb = torch.nn.Embedding(emb_dim, emb_dim) + particle_type_cfg = {"target": target} + flat = _assemble_stage2_real( + sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim + ) + unflat = _assemble_stage2_ar_target( + sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim + ) + assert torch.equal(unflat.flatten(1), flat) + + +def test_assemble_stage2_ar_inputs_shapes_and_history_feat_width(): + B, emb_dim = 3, 6 + sec_cont = torch.randn(B, K_MAX, SEC_SLOT_DIM) + sec_type_idx = torch.randint(0, emb_dim, (B, K_MAX)) + cond_enc = torch.nn.Module() + out = _assemble_stage2_ar_inputs( + sec_cont, sec_type_idx, {"target": "physical"}, cond_enc, emb_dim + ) + assert out["history_feat"].shape == (B, K_MAX, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) + assert out["has_prev"].shape == (B, K_MAX) + assert out["remaining_frac"].shape == (B, K_MAX) + assert out["slot_idx"].shape == (B, K_MAX) + assert torch.all(out["slot_idx"][:, 0] == 0.0) + assert torch.all(out["slot_idx"][:, -1] == 1.0) + + +def test_relax_onehot_type_slice_grad_probe_populates_both_norms(): + B, k_max, cont_dim, type_dim = 4, K_MAX, CONT_SLOT_DIM, 6 + x_flat = torch.randn(B, k_max * (cont_dim + type_dim), requires_grad=True) + grad_probe: dict[str, float] = {} + out = _relax_onehot_type_slice( + x_flat, k_max, cont_dim, type_dim, tau=0.5, grad_probe=grad_probe + ) + out.sum().backward() + assert grad_probe["cont"] >= 0.0 + assert grad_probe["type"] >= 0.0 + + +def test_relax_onehot_type_slice_grad_probe_none_is_backward_compatible(): + B, k_max, cont_dim, type_dim = 4, K_MAX, CONT_SLOT_DIM, 6 + x_flat = torch.randn(B, k_max * (cont_dim + type_dim), requires_grad=True) + out = _relax_onehot_type_slice(x_flat, k_max, cont_dim, type_dim, tau=0.5) + out.sum().backward() + assert x_flat.grad is not None + + # --- end-to-end train() integration tests ----------------------------------- PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} @@ -273,6 +407,59 @@ def _run_train(cfg, out_dir, resume_path=None): ), ), ), + ( + "ar_wgan_onehot", + lambda cfg: ( + cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "onehot", "lambda": 1.0} + ), + ), + ), + ( + "ar_wgan_physical", + lambda cfg: cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + ), + ( + "ar_flow_onehot", + lambda cfg: ( + cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + cfg["stage2_model"].__setitem__("generator", "flow"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "onehot", "lambda": 1.0} + ), + ), + ), + ( + "ar_flow_embedding", + lambda cfg: ( + cfg["conditioning"]["particle"].__setitem__("type", "embedding"), + cfg["conditioning"]["material"].__setitem__("type", "embedding"), + cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + cfg["stage2_model"].__setitem__("generator", "flow"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "embedding", "lambda": 1.0} + ), + ), + ), + ( + "ar_stage2_only", + lambda cfg: ( + cfg["stage1_model"].__setitem__("active", False), + cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + ), + ), + ( + "ar_mixed_stage1_wgan_stage2_flow_onehot", + lambda cfg: ( + cfg["stage1_model"].__setitem__("generator", "wgan"), + cfg["stage2_model"].__setitem__("generator", "flow"), + cfg["stage2_model"].__setitem__("decoder", "autoregressive"), + cfg["stage2_model"].__setitem__( + "particle_type", {"target": "onehot", "lambda": 1.0} + ), + ), + ), ], ) def test_train_end_to_end(label, mutate): @@ -400,3 +587,66 @@ def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): ddpm_n_steps=50, device=torch.device("cpu"), ) + + +# --- AR trainer wiring (v0.3.0 step 5) -------------------------------------- + + +def test_build_stage_trainers_rejects_scheduled_teacher_forcing(): + cfg = _base_cfg() + cfg["stage2_model"]["decoder"] = "autoregressive" + cfg["stage2_model"]["autoregressive"] = { + "history": "markov", + "teacher_forcing": "scheduled", + } + model_config = _model_config(cfg) + models = build_models(model_config) + critics = build_critics(model_config) + with pytest.raises(NotImplementedError): + _build_stage_trainers( + cfg, models, critics, torch.device("cpu"), total_train_batches=4 + ) + + +def test_ar_wgan_onehot_grad_norm_instrumentation_populates_metrics(): + """§11.4 differentiability validation-obligation instrumentation: the + trunk-gradient-norm-by-slice columns must appear and actually fire for + generator='wgan' + particle_type.target='onehot' under decoder= + 'autoregressive' (added at v0.3.0 step 5 per the design doc's + instruction to accrue evidence during the architecture comparison).""" + cfg = _base_cfg() + cfg["stage2_model"]["decoder"] = "autoregressive" + cfg["stage2_model"]["particle_type"] = {"target": "onehot", "lambda": 1.0} + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + with open(out_dir / "metrics.csv", newline="") as f: + rows = list(csv.DictReader(f)) + assert "stage2_train_grad_norm_type_slice" in rows[0] + assert "stage2_train_grad_norm_cont_slice" in rows[0] + assert any(float(r["stage2_train_grad_norm_type_slice"]) > 0 for r in rows) + assert any(float(r["stage2_train_grad_norm_cont_slice"]) > 0 for r in rows) + + +def test_wgan_onehot_one_shot_also_gets_grad_norm_instrumentation(): + """The instrumentation is decoder-agnostic — one_shot + wgan + onehot + must populate the same columns.""" + cfg = _base_cfg() + cfg["stage2_model"]["particle_type"] = {"target": "onehot", "lambda": 1.0} + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + with open(out_dir / "metrics.csv", newline="") as f: + rows = list(csv.DictReader(f)) + assert any(float(r["stage2_train_grad_norm_type_slice"]) > 0 for r in rows) + assert any(float(r["stage2_train_grad_norm_cont_slice"]) > 0 for r in rows) + + +def test_wgan_physical_omits_grad_norm_slice_columns(): + cfg = _base_cfg() # default stage2_model has no particle_type -> "physical" + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + header = (out_dir / "metrics.csv").read_text().splitlines()[0].split(",") + assert "stage2_train_grad_norm_type_slice" not in header + assert "stage2_train_grad_norm_cont_slice" not in header -- 2.39.5 From 93b19911f8e6370999f7eade04b1e1532bc392d3 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 7 Aug 2026 10:37:57 +0200 Subject: [PATCH 09/30] v0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - giant/sample.py: fix every sampler's call convention against Stage1Model/Stage2OneShot's actual forward signatures (was still calling model(x, t, cond_cont, cond_cat) positionally); add sample_secondaries_ar (free-running AR loop, unsnapped history feature) and sample_stage1/sample_stage2/resolve_n_sec dispatch helpers that read each stage's generator_kind/decoder off the model instance itself. - giant/particles.py: decode_topn_class (argmax + other_policy) and decode_embedding_nearest (L1-snap + distance) turn a secondary's "onehot"/"embedding" type prediction into a concrete PDG. - giant/rollout.py: decode_secondary_identity routes all three particle_type.target values to real mass/charge; per-stage generator dispatch (drops the single shared `mode` string, adds ddpm support); L1DistCollector accumulates the §11.3 embedding-distance diagnostic. - giant/cli.py: drop the onehot/embedding-target rejection gate (narrowed to the still-unimplemented conditioning.particle/material.type=onehot axis); fix the dead model_cfg.get("mode") bug in predict/rollout. - giant/analysis/: new type_embedding_l1_distance PlotSpec, wired through the rollout YAML sidecar (no live-model call needed, unlike router_gating -- the histogram is already pre-aggregated at rollout time). - Un-xfail every test that was blocked on this step (test_rollout.py, test_flow.py, test_wgan.py, test_phase2.py, test_router.py, test_validate.py); add test_sample.py, test_type_embedding_distance.py. Known follow-up: giant/validate.py still unpacks the training val-batch as a stale 6-tuple and doesn't use the new per-stage dispatch, so marginal validation during training degrades gracefully with a warning rather than working -- not in this step's scope. Co-Authored-By: Claude Sonnet 5 --- giant/analysis/catalog.py | 18 + giant/analysis/condor.py | 15 +- giant/analysis/render.py | 2 + giant/analysis/type_embedding_distance.py | 70 ++++ giant/cli.py | 173 +++++--- giant/data/transforms.py | 91 ++-- giant/model/network.py | 2 + giant/particles.py | 122 ++++++ giant/rollout.py | 259 ++++++++++-- giant/sample.py | 489 +++++++++++++++++----- giant/train.py | 28 +- tests/test_cli_predict.py | 37 +- tests/test_flow.py | 10 - tests/test_particles.py | 102 +++++ tests/test_phase2.py | 9 - tests/test_rollout.py | 265 +++++++++++- tests/test_router.py | 33 +- tests/test_sample.py | 237 +++++++++++ tests/test_type_embedding_distance.py | 43 ++ tests/test_validate.py | 10 - 20 files changed, 1696 insertions(+), 319 deletions(-) create mode 100644 giant/analysis/type_embedding_distance.py create mode 100644 tests/test_sample.py create mode 100644 tests/test_type_embedding_distance.py diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index fc665e3..8f5c751 100644 --- a/giant/analysis/catalog.py +++ b/giant/analysis/catalog.py @@ -58,6 +58,7 @@ from giant.analysis.router_gating import ( compute_router_share_by_process, ) from giant.analysis.sources import Side, open_side, physical_steps, secondaries +from giant.analysis.type_embedding_distance import compute_type_embedding_l1_distance from giant.analysis.variables import RANGED_VARS, cos_scatter_expr @@ -71,6 +72,11 @@ class Bundle: r_phys: pl.LazyFrame # rollout, physical steps only t_phys: pl.LazyFrame # reference, physical steps only checkpoint: str | None = None # from the rollout YAML; router_gating only + # §11.3 diagnostic pre-aggregated at rollout time (giant.rollout. + # L1DistCollector.summary()) — from the rollout YAML, type_embedding_l1_distance + # only. Unlike checkpoint/router_gating, this needs no live model: it's + # already a finished histogram, just passed through. + type_embedding_l1_dist: dict | None = None @classmethod def open( @@ -80,6 +86,7 @@ class Bundle: ctx: Context, checkpoint=None, chunk: tuple[int, int] | None = None, + type_embedding_l1_dist: dict | None = None, ) -> "Bundle": """Open both sides, optionally restricted to one event-disjoint chunk. @@ -103,6 +110,7 @@ class Bundle: r_phys=physical_steps(r_all, Side.rollout), t_phys=physical_steps(t_all, Side.reference), checkpoint=checkpoint, + type_embedding_l1_dist=type_embedding_l1_dist, ) @@ -658,6 +666,9 @@ _router_share_pdg_partial, _router_share_pdg_finalize = _unchunkable( _router_share_process_partial, _router_share_process_finalize = _unchunkable( lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys) ) +_type_embedding_l1_distance_partial, _type_embedding_l1_distance_finalize = ( + _unchunkable(lambda b: compute_type_embedding_l1_distance(b.type_embedding_l1_dist)) +) # --------------------------------------------------------------------------- @@ -831,6 +842,13 @@ def build_catalog() -> list[PlotSpec]: finalize=_router_share_process_finalize, chunkable=False, ), + PlotSpec( + "type_embedding_l1_distance", + "model", + compute_partial=_type_embedding_l1_distance_partial, + finalize=_type_embedding_l1_distance_finalize, + chunkable=False, + ), ] return specs diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 0312ca4..ec2d3e0 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -83,6 +83,12 @@ _PLOT_META_KEYS = ( "best_val_loss", "training_config", "training_meta", + # §11.3 diagnostic — only present when giant rollout ran under + # stage2_model.particle_type.target="embedding" (see giant/cli.py's + # rollout command and giant.rollout.L1DistCollector); absent otherwise, + # which the type_embedding_l1_distance PlotSpec (catalog.py) reads as + # "not applicable to this checkpoint". + "type_embedding_l1_dist", ) @@ -247,6 +253,7 @@ def compute_reduced( checkpoint: str | None = None, chunk_index: int = 0, n_chunks: int = 1, + type_embedding_l1_dist: dict | None = None, ) -> Path: """Core: run one (plot, chunk)'s partial reduction against explicit paths. @@ -265,7 +272,12 @@ def compute_reduced( f"n_chunks={effective_n} (chunkable={spec.chunkable})" ) bundle = Bundle.open( - rollout, reference, ctx, checkpoint=checkpoint, chunk=(chunk_index, effective_n) + rollout, + reference, + ctx, + checkpoint=checkpoint, + chunk=(chunk_index, effective_n), + type_embedding_l1_dist=type_embedding_l1_dist, ) partial = Partial( id=spec_id, @@ -291,6 +303,7 @@ def compute_one(spec_id: str, run_dir: str | Path, chunk_index: int = 0) -> Path checkpoint=meta.plot_meta.get("checkpoint"), chunk_index=chunk_index, n_chunks=meta.n_chunks, + type_embedding_l1_dist=meta.plot_meta.get("type_embedding_l1_dist"), ) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index b746942..ec166ca 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -144,6 +144,8 @@ def _render_single(r: Reduced, params: dict): ) if r.payload.get("log_y"): ax.set_yscale("log") + if r.payload.get("log_x"): + ax.set_xscale("log") ax.set_xlabel(r.xlabel) ax.set_ylabel("density") ps.style_legend(ax, title="source") diff --git a/giant/analysis/type_embedding_distance.py b/giant/analysis/type_embedding_distance.py new file mode 100644 index 0000000..adb39c6 --- /dev/null +++ b/giant/analysis/type_embedding_distance.py @@ -0,0 +1,70 @@ +"""Secondary-type embedding-distance diagnostic (docs/v0.3.0-design.md §11.3). + +Unlike every other diagnostic in this package, the data isn't derivable from +a rollout/reference parquet at all — it's the L1 distance between each +emitted secondary's *raw* predicted embedding vector (under +`stage2_model.particle_type.target = "embedding"`) and the nearest row of the +conditioning's embedding table it snapped to, which only exists transiently +inside `giant rollout` (`giant.rollout.decode_secondary_identity`), never +written to a column. So it's accumulated once, at rollout time +(`giant.rollout.L1DistCollector`), and stashed as a pre-finished histogram +summary in the rollout YAML sidecar (`type_embedding_l1_dist`) — this module +just turns that summary into a `Reduced`, no parquet scan involved (a +`chunkable=False` spec, like `router_gating`, but even cheaper: no live model +call either). + +A heavy right tail means the decoder is emitting vectors off the embedding +manifold — the direct analogue of the species-collapse symptom the v0.3.0 +redesign exists to fix. +""" + +from __future__ import annotations + +from giant.analysis.reduced import Reduced + +_NOTE_NOT_APPLICABLE = ( + "not applicable: this rollout's checkpoint doesn't use " + "stage2_model.particle_type.target='embedding' (or generated no " + "secondaries), so giant rollout recorded no type_embedding_l1_dist " + "diagnostic in its YAML sidecar" +) + + +def compute_type_embedding_l1_distance(l1_dist: dict | None) -> Reduced: + """`Reduced` for the type-embedding-distance figure, or an explanatory + note if this checkpoint never populated the diagnostic. + + `l1_dist`: `giant.rollout.L1DistCollector.summary()`'s dict, as recorded + in the rollout YAML's `type_embedding_l1_dist` key (`Bundle. + type_embedding_l1_dist`) — `{"n", "mean", "std", "min", "max", + "hist_edges", "hist_counts"}`. + """ + if l1_dist is None: + return Reduced( + id="type_embedding_l1_distance", + family="model", + kind="unavailable", + title="Secondary-type embedding L1 distance", + xlabel="n/a", + payload={"note": _NOTE_NOT_APPLICABLE}, + ) + + return Reduced( + id="type_embedding_l1_distance", + family="model", + kind="single_hist", + title="Secondary-type embedding L1 distance (predicted vector -> nearest PDG row)", + xlabel="L1 distance", + payload={ + "edges": l1_dist["hist_edges"], + "rollout": l1_dist["hist_counts"], + "log_y": True, + "log_x": True, + "note": ( + f"n={l1_dist['n']:,} mean={l1_dist['mean']:.4g} " + f"std={l1_dist['std']:.4g} min={l1_dist['min']:.4g} " + f"max={l1_dist['max']:.4g}; rollout only, no reference " + "concept for a raw pre-decode vector" + ), + }, + ) diff --git a/giant/cli.py b/giant/cli.py index 99dfdba..d476390 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -34,24 +34,22 @@ from giant.data.loader import ( from giant.data.transforms import ( build_features, build_cond_features, - decode_secondaries, energy_simplex_decode, inv_local_frame_rotation, inv_log_transform, reconstruct_post_pos, Normalizer, ) +from giant.data.setup_cache import topnmap_from_json from giant.geometry import GeometryOracle from giant.model.network import build_models -from giant.particles import nearest_known_pdg from giant.pipeline import run_train_job -from giant.rollout import rollout as run_rollout -from giant.sample import ( - sample_flow, - sample_secondaries, - sample_wgan, - sample_secondaries_wgan, +from giant.rollout import ( + L1DistCollector, + decode_secondary_identity, + rollout as run_rollout, ) +from giant.sample import resolve_n_sec, sample_stage1, sample_stage2 app = typer.Typer(no_args_is_help=True) @@ -86,41 +84,66 @@ def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: return raw -def _check_v030_onehot_support(model_cfg: dict, command: str) -> None: - """`giant predict`/`giant rollout` don't yet support conditioning - `"onehot"` mode or `stage2_model.particle_type.target` in `("onehot", - "embedding")` — full decode (class index -> concrete PDG, `other_policy` - sampling) is v0.3.0 step 6 (docs/v0.3.0-design.md), which also lands the - autoregressive decoder these targets are meant to pair with. Without - this guard, predict/rollout would crash later on a `cond_cat`/`sec_dim` - shape mismatch (onehot) or silently read a meaningless raw (log_mass, - charge) pair (embedding) instead of failing clearly. A v0.2 (flat) - `model_config` never has these, so this is a no-op there. +def _check_conditioning_onehot_support(model_cfg: dict, command: str) -> None: + """`giant predict`/`giant rollout` don't yet support + `conditioning.particle/material.type = "onehot"` — that needs + `pdg_topn_map.class_map`/`mat_topn_map.class_map` threaded into every + `build_cond_features` call in this module and `giant/rollout.py`'s + `_step_chunk`, which hasn't been wired (a separate axis from + `stage2_model.particle_type.target`, handled below — see + docs/v0.3.0-design.md §3.1 vs §3.3). Without this guard, predict/rollout + would silently build a `cond_cat` missing the topN columns + `ConditionEncoder`'s `"onehot"` mode expects. A v0.2 (flat) `model_config` + never has this, so this is a no-op there. """ conditioning = model_cfg.get("conditioning") if not isinstance(conditioning, dict): return particle_type = conditioning.get("particle", {}).get("type") material_type = conditioning.get("material", {}).get("type") - particle_type_target = ( - model_cfg.get("stage2_model", {}).get("particle_type", {}).get("target") - ) - if ( - particle_type == "onehot" - or material_type == "onehot" - or particle_type_target in ("onehot", "embedding") - ): + if particle_type == "onehot" or material_type == "onehot": typer.echo( - f"error: giant {command} does not yet support conditioning " - "onehot mode or stage2_model.particle_type.target in " - "('onehot', 'embedding') — full decode (class index -> concrete " - "PDG) lands in v0.3.0 step 6 alongside the autoregressive " - "decoder these targets are meant to pair with.", + f"error: giant {command} does not yet support " + "conditioning.particle/material.type = 'onehot' — only " + "stage2_model.particle_type.target in ('onehot', 'embedding') " + "is implemented (docs/v0.3.0-design.md step 6).", err=True, ) raise typer.Exit(1) +def _stage_cfg(model_cfg: dict, stage: str) -> dict: + """`model_cfg[f"{stage}_model"]` for a new-format model_config, `{}` for + a v0.2 flat one (whose ddpm schedule always used `CosineSchedule`'s own + default `T=1000` — never a config key — and which never had + `particle_type` at all, so `{}` is the correct fallback for both + `_ddpm_steps`/`_particle_type_other_policy` below).""" + val = model_cfg.get(f"{stage}_model") + return val if isinstance(val, dict) else {} + + +def _ddpm_steps(model_cfg: dict, stage: str) -> int: + return _stage_cfg(model_cfg, stage).get("ddpm", {}).get("n_steps", 1000) + + +def _particle_type_other_policy(model_cfg: dict) -> str: + return ( + _stage_cfg(model_cfg, "stage2") + .get("particle_type", {}) + .get("other_policy", "sample") + ) + + +def _load_pdg_topn_map(ckpt: dict): + """`ckpt["pdg_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if + this checkpoint's conditioning/particle_type never needed one (see + `giant.pipeline.run_setup_stage`, which only populates it when + `conditioning.particle.type` or `stage2_model.particle_type.target` is + `"onehot"`).""" + raw = ckpt.get("pdg_topn_map") + return topnmap_from_json(raw, axis="pdg") if raw is not None else None + + def _batch_size_estimate_dims( model_cfg: dict, training: bool, stage: str = "stage1" ) -> tuple[int, int]: @@ -965,7 +988,10 @@ def predict( raise typer.Exit(1) model_cfg = ckpt["model_config"] - _check_v030_onehot_support(model_cfg, "predict") + _check_conditioning_onehot_support(model_cfg, "predict") + pdg_topn_map = _load_pdg_topn_map(ckpt) + other_policy = _particle_type_other_policy(model_cfg) + stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") if batch_size_auto: est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( @@ -1046,23 +1072,21 @@ def predict( cc = torch.from_numpy(cond_cont).float().to(_device) ck = torch.from_numpy(cond_cat).long().to(_device) - if model_cfg.get("mode") == "wgan": - stage1_norm, n_sec_pred = sample_wgan(model, cc, ck) - else: - stage1_norm, n_sec_pred = sample_flow(model, cc, ck, steps=steps) + stage1_norm, n_sec_pred = sample_stage1( + model, cc, ck, steps=steps, ddpm_steps=stage1_ddpm_steps + ) if coord == Coord.global_: - if model_cfg.get("mode") == "wgan": - sec_cont, sec_phys, _sec_valid_pred = sample_secondaries_wgan( - sec_decoder, cc, ck, stage1_norm, n_sec_pred - ) - else: - sec_cont, sec_phys, _sec_valid_pred = sample_secondaries( - sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps=steps - ) - sec_full_np = torch.cat([sec_cont, sec_phys], dim=-1).cpu().numpy() + # A fresh v0.3.0 Stage1Model owns no n_sec_head (decision 1) — + # sample_stage1 returns n_sec_pred=None then, so ask stage 2. + n_sec_pred = resolve_n_sec( + model, sec_decoder, cc, ck, stage1_norm, n_sec_pred + ) + sec_cont, sec_type, _sec_valid_pred = sample_stage2( + sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps + ) + n_sec_pred_np = n_sec_pred.cpu().numpy() - n_sec_pred_np = n_sec_pred.cpu().numpy() pred = stage1_norm.cpu().numpy() # normalised # Inverse-normalise → local frame, log-scaled scalars @@ -1119,19 +1143,26 @@ def predict( piece["pre_pos"], piece["pre_dir"], step_length, travel_dir_local ) - sec_E, sec_dir_world, sec_mass, sec_charge, _sec_valid = decode_secondaries( - sec_full_np, - n_sec_pred_np, - e_sec_pred, - piece["pre_dir"], - sec_phys_normalizer=sec_phys_norm, + # particle_type.target="physical": sec_pdg_code is a reporting- + # only nearest-known-PDG label (never fed back into the model — + # "no snapping at inference"). "onehot"/"embedding": PDG + # resolution IS the secondary's identity — see + # decode_secondary_identity's docstring. + sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, _l1_dist = ( + decode_secondary_identity( + sec_decoder, + sec_cont, + sec_type, + n_sec_pred_np, + e_sec_pred, + piece["pre_dir"], + sec_phys_norm, + pdg_map, + pdg_topn_map, + other_policy, + None, + ) ) - # Reporting-only nearest-known-PDG label (never fed back into the - # model) for the sec_pdg_list output column — see - # giant/particles.py and the "no snapping at inference" design. - sec_pdg_code = nearest_known_pdg( - sec_mass.reshape(-1), sec_charge.reshape(-1), pdg_map.keys() - ).reshape(sec_mass.shape) sec_pdg_list = [ sec_pdg_code[i, :n].tolist() for i, n in enumerate(n_sec_pred_np) ] @@ -1378,8 +1409,12 @@ def rollout( training_cfg = gconfig.load_checkpoint_config(checkpoint) model_cfg = ckpt["model_config"] - _check_v030_onehot_support(model_cfg, "rollout") + _check_conditioning_onehot_support(model_cfg, "rollout") conditioning = _conditioning_str(model_cfg) + pdg_topn_map = _load_pdg_topn_map(ckpt) + other_policy = _particle_type_other_policy(model_cfg) + stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") + stage2_ddpm_steps = _ddpm_steps(model_cfg, "stage2") pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) @@ -1432,6 +1467,10 @@ def rollout( writer = pq.ParquetWriter(out, table.schema) writer.write_table(table) + # Only meaningful under particle_type.target="embedding" (§11.3) — a + # no-op collector otherwise, cheaper than branching the call itself. + l1_dist_collector = L1DistCollector() + summary = run_rollout( model, sec_decoder, @@ -1451,11 +1490,18 @@ def rollout( escape_threshold=escape_threshold, on_chunk=_write_chunk, conditioning=conditioning, - mode=model_cfg.get("mode", "flow"), + pdg_topn_map=pdg_topn_map, + other_policy=other_policy, + seed=seed, + stage1_ddpm_steps=stage1_ddpm_steps, + stage2_ddpm_steps=stage2_ddpm_steps, + l1_dist_collector=l1_dist_collector, ) if writer is not None: writer.close() + l1_summary = l1_dist_collector.summary() + ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path) ref = yaml.safe_load(ref_path.read_text()) ref.update( @@ -1475,6 +1521,13 @@ def rollout( "rollout_seed": seed, "n_rows": summary["n_rows"], "termination_reason_counts": summary["termination_reason_counts"], + # §11.3 diagnostic — only present under + # stage2_model.particle_type.target="embedding"; omitted (not + # written as null) otherwise, so giant.analysis can tell "not + # applicable to this checkpoint" apart from "collector empty". + **( + {"type_embedding_l1_dist": l1_summary} if l1_summary is not None else {} + ), # Full architecture spec baked into the checkpoint — includes the # entire router sub-dict, not just a hand-picked subset, so any # model knob (router type/n_experts, noise_dim, vocab sizes, ...) diff --git a/giant/data/transforms.py b/giant/data/transforms.py index a605cab..562abf0 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -587,42 +587,37 @@ def encode_secondary_type_idx( return np.where(sec_valid, idx, 0).astype(np.int64) -def decode_secondaries( +def decode_secondary_cont( sec_cont: np.ndarray, n_sec: np.ndarray, e_sec: np.ndarray, pre_dir: np.ndarray, - sec_phys_normalizer: "Normalizer | None" = None, -) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: - """Inverse of encode_secondaries: continuous targets → physical secondary attrs. +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Continuous-only half of `decode_secondaries`'s inverse: the + stick-breaking energy split and local->world direction — generator/ + `particle_type.target`-independent, since every target (`"physical"`, + `"onehot"`, `"embedding"`) shares the same `CONT_SLOT_DIM`-wide + (stick_logit, dir) prefix (docs/v0.3.0-design.md §6.1) and differs only + in what follows it. `decode_secondaries` (target="physical") is the + original all-in-one form built on top of this; `target` in `("onehot", + "embedding")` decodes their type slice separately via + `giant.particles.decode_topn_class`/`decode_embedding_nearest` and calls + this directly instead — see `giant/rollout.py`. - sec_cont: (N, K_MAX, 6) — [stick_logit, local_dir_x, local_dir_y, - local_dir_z, log_mass, charge] (log_mass/charge normalised iff - `sec_phys_normalizer` was applied when this was produced — e.g. a - raw model prediction; pass the same normalizer here to invert it) + sec_cont: (N, K, >=CONT_SLOT_DIM) — only columns `[:, :, :CONT_SLOT_DIM]` + (stick_logit, local dir) are read; a caller may pass its full + per-slot tensor (continuous + type) unsliced. n_sec: (N,) integer secondary counts e_sec: (N,) total secondary energy budget [MeV] pre_dir: (N, 3) pre-step world-frame direction - Returns (sec_E, sec_dir_world, sec_mass, sec_charge, sec_valid) each - shape (N, K_MAX). The valid slots' energies (`sec_E[sec_valid]`, per row) - always sum to exactly `e_sec` — see the rescaling below. mass/charge are - the model's raw predicted physical identity for each secondary, used - as-is (no snapping to a discrete PDG code) — see giant/particles.py for - the separate, reporting-only nearest-PDG lookup callers may apply on top - of this for display/bookkeeping purposes. + Returns (sec_E, sec_dir_world, sec_valid), shapes (N, K), (N, K, 3), + (N, K). The valid slots' energies (`sec_E[sec_valid]`, per row) always + sum to exactly `e_sec` — see the rescaling below. """ - if sec_phys_normalizer is not None: - N_, K_, _ = sec_cont.shape - phys = sec_phys_normalizer.inverse_transform(sec_cont[:, :, 4:6].reshape(-1, 2)) - sec_cont = sec_cont.copy() - sec_cont[:, :, 4:6] = phys.reshape(N_, K_, 2) - - N, K, _ = sec_cont.shape + N, K = sec_cont.shape[0], sec_cont.shape[1] stick_logits = sec_cont[:, :, 0] # (N, K) dir_local = sec_cont[:, :, 1:4].copy() # (N, K, 3) - log_mass = sec_cont[:, :, 4] # (N, K) - charge = sec_cont[:, :, 5] # (N, K) # Flow-matching output isn't guaranteed unit norm; normalise before the # rotation below, which preserves magnitude rather than fixing it up. @@ -666,6 +661,54 @@ def decode_secondaries( pre_dir[valid], dir_local[valid, i] ) + return sec_E, sec_dir_world, sec_valid + + +def decode_secondaries( + sec_cont: np.ndarray, + n_sec: np.ndarray, + e_sec: np.ndarray, + pre_dir: np.ndarray, + sec_phys_normalizer: "Normalizer | None" = None, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Inverse of encode_secondaries: continuous targets → physical secondary attrs. + + `particle_type.target = "physical"` only (the type slice is a raw + (log_mass, charge) regression target folded straight into `sec_cont`) — + `"onehot"`/`"embedding"` decode through `decode_secondary_cont` + + `giant.particles.decode_topn_class`/`decode_embedding_nearest` instead, + since their type slice isn't (log_mass, charge) at all. See + `decode_secondary_cont`'s docstring for why the two share the energy/ + direction logic below. + + sec_cont: (N, K_MAX, 6) — [stick_logit, local_dir_x, local_dir_y, + local_dir_z, log_mass, charge] (log_mass/charge normalised iff + `sec_phys_normalizer` was applied when this was produced — e.g. a + raw model prediction; pass the same normalizer here to invert it) + n_sec: (N,) integer secondary counts + e_sec: (N,) total secondary energy budget [MeV] + pre_dir: (N, 3) pre-step world-frame direction + + Returns (sec_E, sec_dir_world, sec_mass, sec_charge, sec_valid) each + shape (N, K_MAX). mass/charge are the model's raw predicted physical + identity for each secondary, used as-is (no snapping to a discrete PDG + code) — see giant/particles.py for the separate, reporting-only + nearest-PDG lookup callers may apply on top of this for display/ + bookkeeping purposes. + """ + if sec_phys_normalizer is not None: + N_, K_, _ = sec_cont.shape + phys = sec_phys_normalizer.inverse_transform(sec_cont[:, :, 4:6].reshape(-1, 2)) + sec_cont = sec_cont.copy() + sec_cont[:, :, 4:6] = phys.reshape(N_, K_, 2) + + sec_E, sec_dir_world, sec_valid = decode_secondary_cont( + sec_cont, n_sec, e_sec, pre_dir + ) + + log_mass = sec_cont[:, :, 4] # (N, K) + charge = sec_cont[:, :, 5] # (N, K) + # mass is non-negative by construction (inv_log_transform of a real # number is always > 0); clip to 0 for padded/invalid slots rather than # leaving a spurious small positive floor from the log inverse. diff --git a/giant/model/network.py b/giant/model/network.py index 581e1dd..0624e13 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -982,7 +982,9 @@ class Stage2OneShot(nn.Module): super().__init__() self.generator_kind = generator self.noise_dim = noise_dim + self.k_max = k_max self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) + self.type_dim = stage2_type_dim(self.particle_type_cfg, particle_cfg["emb_dim"]) self.cond_enc = ConditionEncoder( pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim ) diff --git a/giant/particles.py b/giant/particles.py index 4e5ea17..7e2d5ac 100644 --- a/giant/particles.py +++ b/giant/particles.py @@ -7,16 +7,28 @@ table involved) for isomer/excited nuclear codes the package's ground-state-only nuclide table doesn't cover — confirmed necessary for ~32% of the nuclear codes actually present in the multi-material dataset (`0932fb02-f2ce-43ca-a4ef-60a2b1221bbc.parquet`). + +Also holds the v0.3.0 stage-2 categorical-type rollout decode (§3.3/§8/§11.3 +of docs/v0.3.0-design.md): `decode_topn_class`/`decode_embedding_nearest` turn +`Stage2Autoregressive`/`Stage2OneShot`'s `"onehot"`/`"embedding"` type +predictions back into concrete PDG codes, the one place a secondary's +categorical/continuous type representation is ever discretized (its +free-running history representation stays unsnapped — see +`giant/sample.py`'s AR loop). """ from __future__ import annotations from functools import lru_cache +from typing import TYPE_CHECKING import numpy as np from particle import InvalidParticle, Particle, ParticleNotFound from particle import pdgid as _pdgid +if TYPE_CHECKING: + from giant.data.loader import TopNMap + # First-pass nuclear mass approximation (A * atomic mass unit); no # binding-energy correction. Only used for codes missing from `particle`'s # ground-state nuclide table -- ground-state codes get the package's real @@ -111,3 +123,113 @@ def nearest_known_pdg(mass: np.ndarray, charge: np.ndarray, candidates) -> np.nd ) ** 2 idx = d2.argmin(axis=1) return codes[idx] + + +def invert_dense_map(m: dict[int, int]) -> dict[int, int]: + """index -> key, inverting a dense, bijective value->index map (`pdg_map`, + or a `TopNMap.class_map`'s non-"other" entries — see `decode_topn_class`, + which needs a *partial* inverse, not this general one, because its "other" + index isn't unique-preimage). `pdg_map` itself is always a true bijection + (`giant.data.loader.build_index_maps_from_files` enumerates the vocab), so + a plain dict-comprehension inversion is exact here — used for + `stage2_model.particle_type.target = "embedding"` decode, whose vocabulary + is the full dense `pdg_map`, not a top-N-plus-other map.""" + return {v: k for k, v in m.items()} + + +def decode_topn_class( + class_idx: np.ndarray, + topn_map: "TopNMap", + n_classes: int, + other_policy: str = "sample", + rng: np.random.Generator | None = None, +) -> np.ndarray: + """`conditioning.particle.type` / `stage2_model.particle_type.target = + "onehot"` inference decode (docs/v0.3.0-design.md §3.3): per-row top-N + class index -> concrete PDG code. + + class_idx: int array, any shape, values in `[0, n_classes)`. + topn_map: the `TopNMap` (`giant.data.loader.build_pdg_topn_map_from_files`) + this class index was built from — `class_map` (PDG -> class, injective + except at the shared "other" index) plus `other_members` (the + empirical within-"other" distribution, needed for `other_policy = + "sample"`/`"modal"`). + n_classes: `conditioning.particle.emb_dim` — the class count; the "other" + bucket is index `n_classes - 1` by construction + (`giant.data.loader._topn_plus_other_map`). + other_policy: `"sample"` draws from `other_members`' empirical frequency; + `"modal"` always the single most common "other" member; `"drop"` + returns PDG `0` for those rows (not a valid PDG code — the caller + must treat it as "no secondary", the same convention as + `TERM_UNKNOWN_PDG` elsewhere in the rollout driver). + + Every non-"other" class index has a unique inverse (the top `n_classes - + 1` keys each got their own index in `_topn_plus_other_map`), so those + rows decode exactly; only "other" rows need `other_policy`. + """ + other_idx = n_classes - 1 + inv = np.zeros(n_classes, dtype=np.int64) + for pdg, idx in topn_map.class_map.items(): + if idx != other_idx: + inv[idx] = pdg + + flat = np.asarray(class_idx, dtype=np.int64).reshape(-1) + out = inv[np.clip(flat, 0, n_classes - 1)] + + other_mask = flat == other_idx + n_other = int(other_mask.sum()) + if n_other: + if not topn_map.other_members: + raise ValueError( + "decode_topn_class: 'other' class predicted but " + "topn_map.other_members is empty" + ) + members = np.array(list(topn_map.other_members.keys()), dtype=np.int64) + counts = np.array(list(topn_map.other_members.values()), dtype=np.float64) + if other_policy == "drop": + out[other_mask] = 0 + elif other_policy == "modal": + out[other_mask] = members[counts.argmax()] + elif other_policy == "sample": + rng = rng if rng is not None else np.random.default_rng() + probs = counts / counts.sum() + out[other_mask] = rng.choice(members, size=n_other, p=probs) + else: + raise ValueError(f"unknown other_policy {other_policy!r}") + + return out.reshape(np.asarray(class_idx).shape) + + +def decode_embedding_nearest( + vectors: np.ndarray, + emb_weight: np.ndarray, + idx_to_pdg: dict[int, int], +) -> tuple[np.ndarray, np.ndarray]: + """`stage2_model.particle_type.target = "embedding"` inference decode + (docs/v0.3.0-design.md §3.3): L1-nearest row of the conditioning's own + particle embedding table, since a generative model's continuous output + essentially never lands within float tolerance of a table row (the exact- + match form is only valid as a round-trip test assertion, never here). + + vectors: `(..., emb_dim)` raw predicted vectors, any leading shape. + emb_weight: `(vocab, emb_dim)` — `ConditionEncoder.pdg_emb.weight`, + detached and moved to numpy by the caller. This is the SAME table + `particle_type.target = "embedding"` was regressed against + (`validate_config` requires `conditioning.particle.type = + "embedding"` whenever this target is used — one table, not two). + idx_to_pdg: `invert_dense_map(pdg_map)` — embedding row index -> PDG. + + Returns `(pdg, l1_dist)`, both shaped like `vectors.shape[:-1]`. `l1_dist` + is the §11.3 diagnostic: a heavy tail means the decoder is emitting + vectors off the embedding manifold, the direct analogue of the species- + collapse symptom this redesign exists to fix. + """ + emb_dim = vectors.shape[-1] + flat = np.asarray(vectors, dtype=np.float64).reshape(-1, emb_dim) + table = np.asarray(emb_weight, dtype=np.float64) + d = np.abs(flat[:, None, :] - table[None, :, :]).sum(axis=-1) # (N, vocab) + nearest = d.argmin(axis=1) + dist = d[np.arange(len(nearest)), nearest] + pdg = np.array([idx_to_pdg[int(i)] for i in nearest], dtype=np.int64) + lead_shape = vectors.shape[:-1] + return pdg.reshape(lead_shape), dist.reshape(lead_shape).astype(np.float32) diff --git a/giant/rollout.py b/giant/rollout.py index 8270a5e..3bca136 100644 --- a/giant/rollout.py +++ b/giant/rollout.py @@ -17,7 +17,7 @@ treated as detector leakage and not deposited. from __future__ import annotations from collections import Counter -from typing import Callable, TypedDict +from typing import TYPE_CHECKING, Callable, TypedDict import numpy as np import torch @@ -33,18 +33,165 @@ from giant.data.transforms import ( Normalizer, build_cond_features, decode_secondaries, + decode_secondary_cont, energy_simplex_decode, inv_local_frame_rotation, inv_log_transform, reconstruct_post_pos, ) -from giant.particles import nearest_known_pdg, particle_phys_array -from giant.sample import ( - sample_flow, - sample_secondaries, - sample_wgan, - sample_secondaries_wgan, +from giant.particles import ( + decode_embedding_nearest, + decode_topn_class, + invert_dense_map, + nearest_known_pdg, + particle_phys_array, ) +from giant.sample import resolve_n_sec, sample_stage1, sample_stage2 + +if TYPE_CHECKING: + from giant.data.loader import TopNMap + + +class L1DistCollector: + """Accumulates the §11.3 L1-distance diagnostic across a whole rollout + run: the L1 distance between each emitted secondary's raw predicted + embedding vector and the nearest table row it snapped to (only + meaningful under `particle_type.target = "embedding"` — + `giant.particles.decode_embedding_nearest`). A heavy tail means the + decoder is emitting vectors off the embedding manifold — the direct + analogue of the species-collapse symptom the v0.3.0 redesign exists to + fix (docs/v0.3.0-design.md §11.3). + + Not folded into `rollout()`'s own return value (which is shape-typed as + step records, see `_RECORD_KEYS`/`RolloutSummary`) — passed in and read + back by the caller instead, mirroring the existing `on_chunk` pattern. + O(1) memory via a fixed log-spaced histogram rather than raw samples, + since a heavy right tail is exactly what this diagnostic watches for. + """ + + def __init__(self, n_bins: int = 50, lo: float = 1e-3, hi: float = 1e3) -> None: + self.n = 0 + self.total = 0.0 + self.total_sq = 0.0 + self.minimum = float("inf") + self.maximum = 0.0 + self.hist_edges = np.geomspace(lo, hi, n_bins + 1) + self.hist_counts = np.zeros(n_bins, dtype=np.int64) + + def add(self, dist: np.ndarray, valid: np.ndarray) -> None: + vals = np.asarray(dist)[np.asarray(valid)] + if vals.size == 0: + return + self.n += int(vals.size) + self.total += float(vals.sum()) + self.total_sq += float(np.square(vals).sum()) + self.minimum = min(self.minimum, float(vals.min())) + self.maximum = max(self.maximum, float(vals.max())) + self.hist_counts += np.histogram(vals, bins=self.hist_edges)[0] + + def summary(self) -> dict | None: + """`None` if nothing was ever added (target != "embedding", or a + run with zero secondaries) — the caller should omit the diagnostic + entirely rather than write a degenerate summary.""" + if self.n == 0: + return None + mean = self.total / self.n + variance = max(self.total_sq / self.n - mean**2, 0.0) + return { + "n": self.n, + "mean": mean, + "std": variance**0.5, + "min": self.minimum, + "max": self.maximum, + "hist_edges": self.hist_edges.tolist(), + "hist_counts": self.hist_counts.tolist(), + } + + +def decode_secondary_identity( + sec_decoder: torch.nn.Module, + sec_cont: torch.Tensor, + sec_type: torch.Tensor, + n_sec_np: np.ndarray, + e_sec: np.ndarray, + pre_dir: np.ndarray, + sec_phys_norm: Normalizer, + pdg_map: dict[int, int], + pdg_topn_map: "TopNMap | None", + other_policy: str, + rng: np.random.Generator | None, +) -> tuple[ + np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray | None +]: + """Decode Stage 2's raw (sec_cont, sec_type) output into physical + secondary attributes, branching on `sec_decoder.particle_type_cfg` + (docs/v0.3.0-design.md §3.3): + + - `"physical"`: unchanged v0.2 path — `sec_type` already *is* (log_mass, + charge), used as the secondary's identity as-is (no snapping). + - `"onehot"`: `sec_type` is per-slot class logits — argmax, then + `giant.particles.decode_topn_class` (+ `other_policy`) resolves a + concrete PDG, whose real physics (log_mass, charge) then come from + `giant.particles.particle_phys_array` — unlike "physical", the PDG + resolution IS the secondary's identity here, not just a reporting + label. + - `"embedding"`: `sec_type` is a raw vector in the conditioning's own + embedding space — `giant.particles.decode_embedding_nearest` L1-snaps + it to the nearest table row for the PDG (+ physics via + `particle_phys_array`), and also returns the L1 distance (§11.3 + diagnostic — see `giant/rollout.py`'s L1-distance accumulator). + + Returns (sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, + sec_type_l1_dist) — the last is `None` except under `"embedding"`. + """ + target = sec_decoder.particle_type_cfg.get("target", "physical") + + if target == "physical": + sec_full = torch.cat([sec_cont, sec_type], dim=-1).cpu().numpy() + sec_E, sec_dir_world, sec_mass, sec_charge, sec_valid = decode_secondaries( + sec_full, n_sec_np, e_sec, pre_dir, sec_phys_normalizer=sec_phys_norm + ) + sec_pdg = nearest_known_pdg( + sec_mass.reshape(-1), sec_charge.reshape(-1), pdg_map.keys() + ).reshape(sec_mass.shape) + return sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, None + + sec_E, sec_dir_world, sec_valid = decode_secondary_cont( + sec_cont.cpu().numpy(), n_sec_np, e_sec, pre_dir + ) + sec_type_np = sec_type.cpu().numpy() + l1_dist = None + + if target == "onehot": + if pdg_topn_map is None: + raise RuntimeError( + "particle_type.target='onehot' rollout needs pdg_topn_map " + "(the checkpoint's saved top-N map) — see ckpt['pdg_topn_map']" + ) + class_idx = sec_type_np.argmax(axis=-1) + sec_pdg = decode_topn_class( + class_idx, + pdg_topn_map, + n_classes=sec_decoder.type_dim, + other_policy=other_policy, + rng=rng, + ) + else: # "embedding" + idx_to_pdg = invert_dense_map(pdg_map) + emb_weight = sec_decoder.cond_enc.pdg_emb.weight.detach().cpu().numpy() + sec_pdg, l1_dist = decode_embedding_nearest(sec_type_np, emb_weight, idx_to_pdg) + l1_dist = np.where(sec_valid, l1_dist, 0.0).astype(np.float32) + + sec_mass, sec_charge = particle_phys_array(sec_pdg.reshape(-1)).T + sec_mass = np.where(sec_valid, sec_mass.reshape(sec_pdg.shape), 0.0).astype( + np.float32 + ) + sec_charge = np.where(sec_valid, sec_charge.reshape(sec_pdg.shape), 0.0).astype( + np.float32 + ) + sec_pdg = np.where(sec_valid, sec_pdg, 0).astype(np.int64) + return sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, l1_dist + # Record columns produced per step / per terminal marker. _RECORD_KEYS = [ @@ -311,7 +458,12 @@ def rollout( escape_threshold: float | None = None, on_chunk: Callable[[dict[str, np.ndarray]], None] | None = None, conditioning: str = "embedding", - mode: str = "flow", + pdg_topn_map: "TopNMap | None" = None, + other_policy: str = "sample", + seed: int | None = None, + stage1_ddpm_steps: int = 1000, + stage2_ddpm_steps: int = 1000, + l1_dist_collector: "L1DistCollector | None" = None, ) -> dict[str, np.ndarray] | RolloutSummary: """Run showers to completion. @@ -324,12 +476,30 @@ def rollout( dict[str, int]}`. Use this for large `--n-events`/`--max-steps` runs, where the full record set would otherwise scale with `n_events * max_steps * avg_tracks_per_event`. + + There is no `mode` parameter — each stage's generative objective is read + directly off the model instance's own `generator_kind` + (docs/v0.3.0-design.md decision 2: stage 1 and stage 2 objectives are + independent, e.g. `stage1_model.generator="flow"` + + `stage2_model.generator="wgan"`), and the decoder (one-shot vs + autoregressive) is inferred from `sec_decoder`'s own class — see + `sample_stage1`/`sample_stage2` (giant.sample). + + `pdg_topn_map`/`other_policy` are only read under + `stage2_model.particle_type.target = "onehot"` (§3.3); `seed` seeds the + `other_policy = "sample"` draw only (torch/numpy sampling itself is + seeded by the caller, same as today). + + `l1_dist_collector`, if given, accumulates the §11.3 embedding-distance + diagnostic across the whole run — see `L1DistCollector`. Only populated + under `particle_type.target = "embedding"`; a no-op otherwise. """ device = device or torch.device("cpu") stage1_model.eval() sec_decoder.eval() if escape_threshold is not None: oracle.escape_threshold = float(escape_threshold) + rng = np.random.default_rng(seed) frontier, counts = make_seed_frontier( seeds["event_id"], @@ -365,7 +535,12 @@ def rollout( device, max_tracks_per_event, conditioning, - mode, + pdg_topn_map, + other_policy, + rng, + stage1_ddpm_steps, + stage2_ddpm_steps, + l1_dist_collector, ) ) frontier = _concat_frontiers(next_parts) @@ -396,7 +571,12 @@ def _step_chunk( device, max_tracks_per_event, conditioning, - mode="flow", + pdg_topn_map, + other_policy, + rng, + stage1_ddpm_steps, + stage2_ddpm_steps, + l1_dist_collector, ) -> dict[str, np.ndarray]: """Advance one chunk of tracks by a single step; return the next frontier.""" n = len(tr["event_id"]) @@ -480,10 +660,9 @@ def _step_chunk( cc = torch.from_numpy(cond_cont).float().to(device) ck = torch.from_numpy(cond_cat).long().to(device) - if mode == "wgan": - stage1_norm, n_sec_pred = sample_wgan(stage1_model, cc, ck) - else: - stage1_norm, n_sec_pred = sample_flow(stage1_model, cc, ck, steps=steps) + stage1_norm, n_sec_pred_stage1 = sample_stage1( + stage1_model, cc, ck, steps, stage1_ddpm_steps + ) raw = tgt_norm.inverse_transform(stage1_norm.cpu().numpy()) step_length = inv_log_transform(raw[:, 0]) @@ -503,32 +682,40 @@ def _step_chunk( tr["pre_pos"], tr["pre_dir"], step_length, travel_dir_local ) + n_sec_pred = resolve_n_sec( + stage1_model, sec_decoder, cc, ck, stage1_norm, n_sec_pred_stage1 + ) n_sec_np = n_sec_pred.cpu().numpy().astype(np.int64) # --- Secondaries --- - # No snapping: sec_mass/sec_charge are the model's raw predicted physical - # identity, used as-is for the spawned track's own future conditioning. - # sec_pdg_code below is a *separate*, reporting-only nearest-known-PDG - # label (never fed back into the model) — see giant/particles.py. - if mode == "wgan": - sec_cont, sec_phys, _valid = sample_secondaries_wgan( - sec_decoder, cc, ck, stage1_norm, n_sec_pred - ) - else: - sec_cont, sec_phys, _valid = sample_secondaries( - sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps=steps - ) - sec_full = torch.cat([sec_cont, sec_phys], dim=-1).cpu().numpy() - sec_E, sec_dir_world, sec_mass, sec_charge, sec_valid = decode_secondaries( - sec_full, - n_sec_np, - e_sec, - tr["pre_dir"], - sec_phys_normalizer=sec_phys_norm, + # No snapping for "physical"/history-facing state elsewhere in the + # pipeline: sec_mass/sec_charge (or, for "onehot"/"embedding", the + # resolved sec_pdg -> real physics) are the secondary's identity, used + # as-is for the spawned track's own future conditioning — see + # decode_secondary_identity's docstring for how each + # particle_type.target differs on whether PDG resolution is a real + # identity decision or just a reporting label. + sec_cont, sec_type, _valid = sample_stage2( + sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps ) - sec_pdg_code = nearest_known_pdg( - sec_mass.reshape(-1), sec_charge.reshape(-1), pdg_map.keys() - ).reshape(sec_mass.shape) + sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, sec_type_l1_dist = ( + decode_secondary_identity( + sec_decoder, + sec_cont, + sec_type, + n_sec_np, + e_sec, + tr["pre_dir"], + sec_phys_norm, + pdg_map, + pdg_topn_map, + other_policy, + rng, + ) + ) + sec_valid = np.arange(sec_E.shape[1])[None, :] < n_sec_np[:, None] + if l1_dist_collector is not None and sec_type_l1_dist is not None: + l1_dist_collector.add(sec_type_l1_dist, sec_valid) edep = edep.astype(np.float64) post_E = post_E.astype(np.float64) diff --git a/giant/sample.py b/giant/sample.py index 271897b..f9ba019 100644 --- a/giant/sample.py +++ b/giant/sample.py @@ -1,26 +1,23 @@ import torch +import torch.nn.functional as F -from giant.constants import K_MAX, SEC_DIM, SEC_SLOT_DIM, X_DIM +from giant.constants import CONT_SLOT_DIM, X_DIM +from giant.model.network import Stage2Autoregressive, stage2_trunk_sec_dim +from giant.model.schedule import CosineSchedule -def _slots_from_flat( - x: torch.Tensor, n_sec_pred: torch.Tensor -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Reshape a flat (B, SEC_DIM) decoder output into per-slot tensors. - - Returns (sec_cont, sec_phys, sec_valid) — see `sample_secondaries`'s - docstring for their shapes/meaning. Shared by both the flow-matching and - WGAN Stage-2 samplers, which differ only in how `x` was produced. - """ - B = x.size(0) - device = x.device - x_slots = x.view(B, K_MAX, SEC_SLOT_DIM) - sec_cont = x_slots[:, :, :4] - sec_phys = x_slots[:, :, 4:] - sec_valid = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec_pred.unsqueeze( - 1 - ) - return sec_cont, sec_phys, sec_valid +def _predict_n_sec_if_owned( + model: torch.nn.Module, cond_cont: torch.Tensor, cond_cat: torch.Tensor +) -> torch.Tensor | None: + """Stage-1 `n_sec_head` is only present on a migrated v0.2 checkpoint + (docs/v0.3.0-design.md decision 1 moves it to stage 2 for fresh runs — + see `Stage1Model`'s docstring). `None` here means "ask stage 2 instead", + which every caller (`giant/rollout.py`, `giant/cli.py`) must do for a + fresh checkpoint.""" + if getattr(model, "n_sec_head", None) is None: + return None + logits = model.predict_n_sec(cond_cont, cond_cat) + return logits.argmax(dim=-1) @torch.no_grad() @@ -29,12 +26,14 @@ def sample_flow( cond_cont: torch.Tensor, cond_cat: torch.Tensor, steps: int = 10, -) -> tuple[torch.Tensor, torch.Tensor]: +) -> tuple[torch.Tensor, torch.Tensor | None]: """Euler integration of the Stage-1 vector field from t=0 to t=1. Returns (primary_sample, n_sec_pred): primary_sample: (B, X_DIM) — normalised 9D primary post-step output - n_sec_pred: (B,) int64 — predicted secondary count + n_sec_pred: (B,) int64 — predicted secondary count, or `None` if + `model` has no `n_sec_head` (a fresh v0.3.0 Stage1Model — see + `_predict_n_sec_if_owned`). """ model.eval() B = cond_cont.size(0) @@ -43,11 +42,142 @@ def sample_flow( dt = 1.0 / steps for i in range(steps): t = torch.full((B,), i * dt, device=device) - v = model(x, t, cond_cont, cond_cat) + v = model(x, cond_cont, cond_cat, t=t) x = x + v * dt - n_sec_logits = model.predict_n_sec(cond_cont, cond_cat) - n_sec_pred = n_sec_logits.argmax(dim=-1) - return x, n_sec_pred + return x, _predict_n_sec_if_owned(model, cond_cont, cond_cat) + + +@torch.no_grad() +def sample_ddpm( + model: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + schedule, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Full DDPM ancestral sampling (T reverse steps). Returns (sample, n_sec_pred) + — see `sample_flow`'s docstring for the `n_sec_pred` `None` case.""" + model.eval() + B = cond_cont.size(0) + device = cond_cont.device + x = torch.randn(B, X_DIM, device=device) + T = schedule.T + for i in reversed(range(T)): + t_norm = torch.full((B,), i / T, device=device) + eps_pred = model(x, cond_cont, cond_cat, t=t_norm) + beta = schedule.betas[i] + alpha = schedule.alphas[i] + alpha_bar = schedule.alpha_bars[i] + z = torch.randn_like(x) if i > 0 else torch.zeros_like(x) + x = (1.0 / alpha.sqrt()) * ( + x - (1.0 - alpha) / (1.0 - alpha_bar).sqrt() * eps_pred + ) + beta.sqrt() * z + return x, _predict_n_sec_if_owned(model, cond_cont, cond_cat) + + +@torch.no_grad() +def sample_ddim( + model: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + schedule, + steps: int = 50, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """DDIM deterministic sampling (Song et al. 2020). Returns (sample, n_sec_pred) + — see `sample_flow`'s docstring for the `n_sec_pred` `None` case.""" + model.eval() + B = cond_cont.size(0) + device = cond_cont.device + T = schedule.T + timesteps = torch.linspace(T - 1, 0, steps, dtype=torch.long, device=device) + x = torch.randn(B, X_DIM, device=device) + for step_idx, ts in enumerate(timesteps): + t_idx = int(ts.item()) + t_norm = torch.full((B,), t_idx / T, device=device) + eps_pred = model(x, cond_cont, cond_cat, t=t_norm) + ab_t = schedule.alpha_bars[t_idx] + if step_idx + 1 < len(timesteps): + ab_prev = schedule.alpha_bars[int(timesteps[step_idx + 1].item())] + else: + ab_prev = torch.ones(1, device=device) + x0_pred = (x - (1.0 - ab_t).sqrt() * eps_pred) / ab_t.sqrt() + x = ab_prev.sqrt() * x0_pred + (1.0 - ab_prev).sqrt() * eps_pred + return x, _predict_n_sec_if_owned(model, cond_cont, cond_cat) + + +@torch.no_grad() +def sample_wgan( + generator: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Single-pass Stage-1 WGAN generator sample. Returns (sample, n_sec_pred) + — see `sample_flow`'s docstring for the `n_sec_pred` `None` case.""" + generator.eval() + B = cond_cont.size(0) + z = torch.randn(B, generator.noise_dim, device=cond_cont.device) + x = generator(z, cond_cont, cond_cat) + return x, _predict_n_sec_if_owned(generator, cond_cont, cond_cat) + + +def _stage2_flat_width(sec_decoder: torch.nn.Module) -> int: + """The width of `sec_decoder`'s own trunk in/out vector — folded + (continuous + type) under `particle_type.target = "physical"` or + `generator = "wgan"`, continuous-only otherwise (the type slice then + comes from `predict_type` instead — see `stage2_trunk_sec_dim`'s + docstring, docs/v0.3.0-design.md decision 2).""" + return stage2_trunk_sec_dim( + sec_decoder.particle_type_cfg, + sec_decoder.generator_kind, + sec_decoder.k_max, + sec_decoder.type_dim, + ) + + +def _type_folded(sec_decoder: torch.nn.Module) -> bool: + target = sec_decoder.particle_type_cfg.get("target", "physical") + return target == "physical" or sec_decoder.generator_kind == "wgan" + + +def _decode_stage2_flat( + sec_decoder: torch.nn.Module, + x: torch.Tensor, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + n_sec_pred: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Reshape a flat `(B, flat_width)` `Stage2OneShot` output into per-slot + tensors, generator/`particle_type.target`-agnostic (docs/v0.3.0-design.md + decision 2/3): shared by `sample_secondaries`/`sample_secondaries_wgan`, + which differ only in how `x` was produced. + + Returns (sec_cont, sec_type, sec_valid): + sec_cont: (B, k_max, CONT_SLOT_DIM) — [stick_logit, local_dir] + sec_type: (B, k_max, type_dim) — under `target="physical"` this is + [log_mass, charge] (normalised iff the checkpoint's sec_phys + normalizer was applied at training time — denormalize before + treating as physical units; see + giant.data.transforms.decode_secondaries); under `"onehot"` / + `"embedding"` it is raw class logits / an embedding-space vector + — decode via giant.particles.decode_topn_class / + decode_embedding_nearest (see giant/rollout.py). + sec_valid: (B, k_max) bool — True for slots i < n_sec_pred + """ + B = x.size(0) + device = x.device + k_max = sec_decoder.k_max + type_dim = sec_decoder.type_dim + if _type_folded(sec_decoder): + x_slots = x.view(B, k_max, CONT_SLOT_DIM + type_dim) + sec_cont = x_slots[:, :, :CONT_SLOT_DIM] + sec_type = x_slots[:, :, CONT_SLOT_DIM:] + else: + sec_cont = x.view(B, k_max, CONT_SLOT_DIM) + sec_type = sec_decoder.predict_type(cond_cont, cond_cat, stage1_out) + sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze( + 1 + ) + return sec_cont, sec_type, sec_valid @torch.no_grad() @@ -59,76 +189,27 @@ def sample_secondaries( n_sec_pred: torch.Tensor, steps: int = 10, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Euler integration of the Stage-2 vector field; return raw slot outputs. + """Euler integration of `Stage2OneShot`'s (flow/ddpm) vector field; return + raw slot outputs — see `_decode_stage2_flat`'s docstring for the returned + (sec_cont, sec_type, sec_valid) shapes/meaning. n_sec_pred: (B,) int64 — number of valid secondaries per step - - Returns (sec_cont, sec_phys, sec_valid): - sec_cont: (B, K_MAX, 4) — [stick_logit, local_dir_x, local_dir_y, local_dir_z] - sec_phys: (B, K_MAX, PARTICLE_PHYS_DIM) — predicted [log_mass, charge] - per slot (normalised iff the checkpoint's sec_phys - normalizer was applied at training time — denormalize - before treating as physical units; see - giant.data.transforms.decode_secondaries). Used as-is — - no snapping to a discrete PDG code. - sec_valid: (B, K_MAX) bool — True for slots i < n_sec_pred """ sec_decoder.eval() B = cond_cont.size(0) device = cond_cont.device + flat_width = _stage2_flat_width(sec_decoder) - x = torch.randn(B, SEC_DIM, device=device) + x = torch.randn(B, flat_width, device=device) dt = 1.0 / steps for i in range(steps): t = torch.full((B,), i * dt, device=device) - v = sec_decoder(x, t, cond_cont, cond_cat, stage1_out) + v = sec_decoder(x, cond_cont, cond_cat, stage1_out, t=t) x = x + v * dt - return _slots_from_flat(x, n_sec_pred) - - -@torch.no_grad() -def sample_ddpm( - model: torch.nn.Module, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - schedule, -) -> tuple[torch.Tensor, torch.Tensor]: - """Full DDPM ancestral sampling (T reverse steps). Returns (sample, n_sec_pred).""" - model.eval() - B = cond_cont.size(0) - device = cond_cont.device - x = torch.randn(B, X_DIM, device=device) - T = schedule.T - for i in reversed(range(T)): - t_norm = torch.full((B,), i / T, device=device) - eps_pred = model(x, t_norm, cond_cont, cond_cat) - beta = schedule.betas[i] - alpha = schedule.alphas[i] - alpha_bar = schedule.alpha_bars[i] - z = torch.randn_like(x) if i > 0 else torch.zeros_like(x) - x = (1.0 / alpha.sqrt()) * ( - x - (1.0 - alpha) / (1.0 - alpha_bar).sqrt() * eps_pred - ) + beta.sqrt() * z - n_sec_logits = model.predict_n_sec(cond_cont, cond_cat) - n_sec_pred = n_sec_logits.argmax(dim=-1) - return x, n_sec_pred - - -@torch.no_grad() -def sample_wgan( - generator: torch.nn.Module, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, -) -> tuple[torch.Tensor, torch.Tensor]: - """Single-pass Stage-1 WGAN generator sample. Returns (sample, n_sec_pred).""" - generator.eval() - B = cond_cont.size(0) - z = torch.randn(B, generator.noise_dim, device=cond_cont.device) - x = generator(z, cond_cont, cond_cat) - n_sec_logits = generator.predict_n_sec(cond_cont, cond_cat) - n_sec_pred = n_sec_logits.argmax(dim=-1) - return x, n_sec_pred + return _decode_stage2_flat( + sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred + ) @torch.no_grad() @@ -139,41 +220,231 @@ def sample_secondaries_wgan( stage1_out: torch.Tensor, n_sec_pred: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Single-pass Stage-2 WGAN generator sample; see `sample_secondaries`'s - docstring for the returned (sec_cont, sec_phys, sec_valid) shapes.""" + """Single-pass `Stage2OneShot` WGAN generator sample; see + `_decode_stage2_flat`'s docstring for the returned (sec_cont, sec_type, + sec_valid) shapes/meaning.""" sec_decoder.eval() B = cond_cont.size(0) z = torch.randn(B, sec_decoder.noise_dim, device=cond_cont.device) x = sec_decoder(z, cond_cont, cond_cat, stage1_out) - return _slots_from_flat(x, n_sec_pred) + return _decode_stage2_flat( + sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred + ) @torch.no_grad() -def sample_ddim( - model: torch.nn.Module, +def sample_secondaries_ar( + sec_decoder: torch.nn.Module, cond_cont: torch.Tensor, cond_cat: torch.Tensor, - schedule, - steps: int = 50, -) -> tuple[torch.Tensor, torch.Tensor]: - """DDIM deterministic sampling (Song et al. 2020). Returns (sample, n_sec_pred).""" - model.eval() + stage1_out: torch.Tensor, + n_sec_pred: torch.Tensor, + steps: int = 10, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """`Stage2Autoregressive` inference loop (docs/v0.3.0-design.md §6.4): + one token at a time, in descending-energy slot order, `k_max` sequential + calls. Unlike training (teacher forcing, §6.2 point 3 — a single + parallel pass over ground-truth tokens, see + `giant.train._assemble_stage2_ar_inputs`), there is no ground truth at + inference: each token's conditioning is built free-running, from the + PREVIOUS TOKEN'S OWN just-generated output — the train/inference gap + §6.2 point 4 explicitly flags as the cost of markov history's + expressiveness. + + A `{flow,ddpm}` token costs `steps` ODE substeps; `wgan` costs one pass — + §6.4's "K sequential forwards" cost note applies per-token here, not + once, so a flow/ddpm AR run costs ~`k_max * steps` model calls per + physics step. + + The free-running history feature stays UNSNAPPED (mirrors the + established "no snapping" precedent for `particle_type.target = + "physical"` secondaries feeding their own future conditioning): + `"physical"` carries the raw (log_mass, charge) forward as-is; + `"embedding"` carries the raw predicted vector as-is; `"onehot"` is the + one exception — its history slot must be a probability-simplex-shaped + vector (that's what `MarkovHistory` was trained on, `_type_repr`'s + `F.one_hot` ground truth), so it's the hard one-hot of `argmax(logits)`, + not the raw logits themselves. Discretizing further, into a concrete PDG + code, only ever happens once — at secondary-spawn time in + `giant/rollout.py` — never inside this loop. + + Returns (sec_cont, sec_type, sec_valid) — same shapes/meaning as + `sample_secondaries`/`sample_secondaries_wgan`'s (see + `_decode_stage2_flat`'s docstring); `sec_type` is raw per-slot output in + all three `particle_type.target` cases (never one-hot-collapsed), so the + caller decodes it exactly the same way regardless of which decoder + produced it. + """ + sec_decoder.eval() B = cond_cont.size(0) device = cond_cont.device - T = schedule.T - timesteps = torch.linspace(T - 1, 0, steps, dtype=torch.long, device=device) - x = torch.randn(B, X_DIM, device=device) - for step_idx, ts in enumerate(timesteps): - t_idx = int(ts.item()) - t_norm = torch.full((B,), t_idx / T, device=device) - eps_pred = model(x, t_norm, cond_cont, cond_cat) - ab_t = schedule.alpha_bars[t_idx] - if step_idx + 1 < len(timesteps): - ab_prev = schedule.alpha_bars[int(timesteps[step_idx + 1].item())] + k_max = sec_decoder.k_max + type_dim = sec_decoder.type_dim + generator = sec_decoder.generator_kind + target = sec_decoder.particle_type_cfg.get("target", "physical") + type_folded = _type_folded(sec_decoder) + token_dim = CONT_SLOT_DIM + type_dim if type_folded else CONT_SLOT_DIM + + sec_cont = torch.zeros(B, k_max, CONT_SLOT_DIM, device=device) + sec_type = torch.zeros(B, k_max, type_dim, device=device) + + # Running per-token state, threaded from one slot to the next. + prev_repr = torch.zeros(B, CONT_SLOT_DIM + type_dim, device=device) + remaining = torch.ones(B, device=device) + + for k in range(k_max): + has_prev = torch.full((B, 1), k >= 1, dtype=torch.bool, device=device) + history_feat = prev_repr.unsqueeze(1) # (B, 1, CONT_SLOT_DIM + type_dim) + remaining_frac = remaining.unsqueeze(1) # (B, 1) + slot_idx = torch.full( + (B, 1), k / max(k_max - 1, 1), device=device, dtype=torch.float32 + ) + + if generator == "wgan": + z = torch.randn(B, 1, sec_decoder.noise_dim, device=device) + token = sec_decoder( + z, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ) else: - ab_prev = torch.ones(1, device=device) - x0_pred = (x - (1.0 - ab_t).sqrt() * eps_pred) / ab_t.sqrt() - x = ab_prev.sqrt() * x0_pred + (1.0 - ab_prev).sqrt() * eps_pred - n_sec_logits = model.predict_n_sec(cond_cont, cond_cat) - n_sec_pred = n_sec_logits.argmax(dim=-1) - return x, n_sec_pred + x = torch.randn(B, 1, token_dim, device=device) + dt = 1.0 / steps + for i in range(steps): + t = torch.full((B, 1), i * dt, device=device) + v = sec_decoder( + x, + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + t=t, + ) + x = x + v * dt + token = x + + token = token.squeeze(1) # (B, token_dim) + cont_k = token[:, :CONT_SLOT_DIM] + if type_folded: + type_k = token[:, CONT_SLOT_DIM:] + else: + type_k = sec_decoder.predict_type( + cond_cont, + cond_cat, + stage1_out, + history_feat, + has_prev, + remaining_frac, + slot_idx, + ).squeeze(1) + + sec_cont[:, k] = cont_k + sec_type[:, k] = type_k + + if target == "onehot": + type_for_history = F.one_hot( + type_k.argmax(dim=-1), num_classes=type_dim + ).float() + else: + type_for_history = type_k + + stick_fraction = torch.sigmoid(cont_k[:, 0]) + prev_repr = torch.cat( + [stick_fraction.unsqueeze(-1), cont_k[:, 1:4], type_for_history], dim=-1 + ) + remaining = torch.clamp(remaining * (1.0 - stick_fraction), min=0.0) + + sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze( + 1 + ) + return sec_cont, sec_type, sec_valid + + +# --------------------------------------------------------------------------- +# Per-stage dispatch — shared by giant/rollout.py and giant/cli.py's +# `predict` command, since both need "given a stage model, produce a +# sample" without hand-picking the sampler themselves (docs/v0.3.0-design.md +# decision 2: each stage's generative objective is independent, read off the +# model's own `generator_kind`, not a caller-supplied `mode` string). +# --------------------------------------------------------------------------- + + +def sample_stage1( + stage1_model: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + steps: int, + ddpm_steps: int = 1000, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Dispatches on `stage1_model.generator_kind`.""" + kind = stage1_model.generator_kind + if kind == "wgan": + return sample_wgan(stage1_model, cond_cont, cond_cat) + if kind == "ddpm": + schedule = CosineSchedule(T=ddpm_steps).to(cond_cont.device) + return sample_ddpm(stage1_model, cond_cont, cond_cat, schedule) + return sample_flow(stage1_model, cond_cont, cond_cat, steps=steps) + + +def sample_stage2( + sec_decoder: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + n_sec_pred: torch.Tensor, + steps: int, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Dispatches on `decoder` (one-shot vs autoregressive — the class + itself, via `isinstance`) and `sec_decoder.generator_kind` (flow/ddpm/ + wgan). DDPM secondaries aren't supported — no `Stage2*` class was ever + built with `generator="ddpm"` in practice and `flow_matching_loss_secondary*` + is the only stage-2 training path that exists for the non-adversarial + case, so there's nothing to dispatch to here. + """ + if isinstance(sec_decoder, Stage2Autoregressive): + return sample_secondaries_ar( + sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps + ) + if sec_decoder.generator_kind == "wgan": + return sample_secondaries_wgan( + sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred + ) + return sample_secondaries( + sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps + ) + + +def resolve_n_sec( + stage1_model: torch.nn.Module, + sec_decoder: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_out: torch.Tensor, + n_sec_pred: torch.Tensor | None, +) -> torch.Tensor: + """`n_sec_pred` is already populated when `stage1_model` owns a legacy + `n_sec_head` (a migrated v0.2 checkpoint — see `Stage1Model`'s + docstring); otherwise ask stage 2, which owns it by default under + decision 1 (docs/v0.3.0-design.md §2). Raises if neither stage owns a + head at all — the only way that happens is `stage2_model.n_sec.mode` + other than `"head"` (`"truth"`/`"stop_token"`), neither of which is a + valid rollout-/predict-capable checkpoint (§3.3, §9).""" + if n_sec_pred is not None: + return n_sec_pred + if getattr(sec_decoder, "n_sec_head", None) is None: + raise RuntimeError( + "checkpoint has no n_sec_head on either stage — needs " + "stage2_model.n_sec.mode = 'head' (the default); 'truth' is " + "standalone-evaluation-only and 'stop_token' isn't implemented " + "(docs/v0.3.0-design.md §3.3/§9)" + ) + logits = sec_decoder.predict_n_sec(cond_cont, cond_cat, stage1_out) + return logits.argmax(dim=-1) diff --git a/giant/train.py b/giant/train.py index a117d2f..a504056 100644 --- a/giant/train.py +++ b/giant/train.py @@ -991,27 +991,31 @@ _WARNED_MARGINAL_VALIDATION_BROKEN = False def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): - """`validate_marginals` delegates to `giant.sample`'s samplers, which - still assume Stage 1 always owns `n_sec_head` (v0.2 behaviour) — under - decision 1 (docs/v0.3.0-design.md §2) a fresh Stage2OneShot owns it by - default instead, so this currently raises for any non-legacy checkpoint. - `giant/sample.py` needs a per-stage generator/n_sec update (design doc - §10, deferred to step 6); until then this degrades gracefully with a - one-time warning instead of crashing the whole training run. + """`giant.sample` itself has a per-stage generator/n_sec-ownership + update (v0.3.0 step 6 — `sample_stage1`/`sample_stage2`/`resolve_n_sec`, + docs/v0.3.0-design.md §10); `giant/validate.py` hasn't been updated to + call it yet, and also still unpacks the val batch as a 6-tuple, which no + longer matches `StreamingStepsDataset`'s 7-tuple shape (the `sec_type_idx` + column step 4 added). Both are `giant/validate.py`-side gaps, not + `giant/sample.py`'s — tracked, not yet scheduled. Until fixed, this + degrades gracefully with a one-time warning instead of crashing the + whole training run. """ global _WARNED_MARGINAL_VALIDATION_BROKEN model = trainer.sampling_model() try: return validate_marginals(model, val_loader, device=device, **kwargs) except Exception as exc: # noqa: BLE001 — see docstring: any failure here - # is expected until giant/sample.py's per-stage n_sec update lands. + # is expected until giant/validate.py is updated for the v0.3.0 + # per-stage dispatch + 7-tuple batch shape. if not _WARNED_MARGINAL_VALIDATION_BROKEN: warnings.warn( "marginal validation unavailable this run " - f"({type(exc).__name__}: {exc}) — giant/sample.py doesn't yet " - "support a Stage2-owned n_sec head (docs/v0.3.0-design.md " - "step 6); val_marginal_kl stays NaN, and wgan best-checkpoint " - "selection falls back to the Wasserstein-distance magnitude.", + f"({type(exc).__name__}: {exc}) — giant/validate.py hasn't " + "been updated for v0.3.0's per-stage sample dispatch / " + "7-tuple val batch shape yet; val_marginal_kl stays NaN, and " + "wgan best-checkpoint selection falls back to the " + "Wasserstein-distance magnitude.", stacklevel=2, ) _WARNED_MARGINAL_VALIDATION_BROKEN = True diff --git a/tests/test_cli_predict.py b/tests/test_cli_predict.py index 8bae93f..1ee8af6 100644 --- a/tests/test_cli_predict.py +++ b/tests/test_cli_predict.py @@ -6,14 +6,14 @@ import yaml from giant.cli import ( _CEPH_PREDICTIONS, - _check_v030_onehot_support, + _check_conditioning_onehot_support, _resolve_prediction_output, _write_prediction_ref, ) # --------------------------------------------------------------------------- -# _check_v030_onehot_support +# _check_conditioning_onehot_support # --------------------------------------------------------------------------- @@ -29,42 +29,43 @@ def _nested_model_cfg( } -def test_check_v030_onehot_support_allows_physical(): - _check_v030_onehot_support(_nested_model_cfg(), "predict") # no raise +def test_check_conditioning_onehot_support_allows_physical(): + _check_conditioning_onehot_support(_nested_model_cfg(), "predict") # no raise -def test_check_v030_onehot_support_rejects_onehot_particle_conditioning(): +def test_check_conditioning_onehot_support_rejects_onehot_particle_conditioning(): cfg = _nested_model_cfg(particle_type="onehot") with pytest.raises(typer.Exit): - _check_v030_onehot_support(cfg, "predict") + _check_conditioning_onehot_support(cfg, "predict") -def test_check_v030_onehot_support_rejects_onehot_material_conditioning(): +def test_check_conditioning_onehot_support_rejects_onehot_material_conditioning(): cfg = _nested_model_cfg(material_type="onehot") with pytest.raises(typer.Exit): - _check_v030_onehot_support(cfg, "rollout") + _check_conditioning_onehot_support(cfg, "rollout") -def test_check_v030_onehot_support_rejects_onehot_particle_type_target(): +def test_check_conditioning_onehot_support_allows_onehot_particle_type_target(): + """stage2_model.particle_type.target="onehot" is implemented (v0.3.0 + step 6, giant.rollout.decode_secondary_identity) — it's a separate axis + from conditioning.particle.type, which this guard doesn't gate at all.""" cfg = _nested_model_cfg(target="onehot") - with pytest.raises(typer.Exit): - _check_v030_onehot_support(cfg, "predict") + _check_conditioning_onehot_support(cfg, "predict") # no raise -def test_check_v030_onehot_support_rejects_embedding_particle_type_target(): +def test_check_conditioning_onehot_support_allows_embedding_particle_type_target(): cfg = _nested_model_cfg( particle_type="embedding", material_type="embedding", target="embedding" ) - with pytest.raises(typer.Exit): - _check_v030_onehot_support(cfg, "predict") + _check_conditioning_onehot_support(cfg, "predict") # no raise -def test_check_v030_onehot_support_is_noop_for_v02_flat_model_config(): +def test_check_conditioning_onehot_support_is_noop_for_v02_flat_model_config(): """A v0.2 checkpoint's flat model_config has conditioning as a plain - string, not a dict — never onehot/embedding-target, so this must be a - silent no-op rather than crash on `.get("particle")` against a string.""" + string, not a dict — never onehot, so this must be a silent no-op rather + than crash on `.get("particle")` against a string.""" cfg = {"conditioning": "embedding", "mode": "flow"} - _check_v030_onehot_support(cfg, "predict") # no raise + _check_conditioning_onehot_support(cfg, "predict") # no raise # --------------------------------------------------------------------------- diff --git a/tests/test_flow.py b/tests/test_flow.py index 6a6730e..f1276f0 100644 --- a/tests/test_flow.py +++ b/tests/test_flow.py @@ -1,4 +1,3 @@ -import pytest import torch from giant.constants import COND_DIM from giant.model.network import Stage1Model @@ -8,13 +7,6 @@ from giant.sample import sample_flow, sample_ddim PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} -_SAMPLE_XFAIL_REASON = ( - "giant/sample.py isn't updated yet — its sample_flow/sample_ddim call " - "models positionally as model(x, t, cond_cont, cond_cat), which doesn't " - "match Stage1Model's new forward signature. Deferred to " - "docs/v0.3.0-design.md step 6." -) - def _small_model(): return Stage1Model( @@ -54,7 +46,6 @@ def test_flow_matching_loss_has_grad(): assert any(p.grad is not None for p in model.parameters()) -@pytest.mark.xfail(reason=_SAMPLE_XFAIL_REASON, strict=False) def test_sample_flow_shape(): B = 6 cond_cont = torch.randn(B, COND_DIM) @@ -71,7 +62,6 @@ def test_ddpm_loss_nonneg(): assert loss.item() >= 0.0 -@pytest.mark.xfail(reason=_SAMPLE_XFAIL_REASON, strict=False) def test_sample_ddim_shape(): B = 4 schedule = CosineSchedule(T=50) diff --git a/tests/test_particles.py b/tests/test_particles.py index 224e2c8..bcfbfdf 100644 --- a/tests/test_particles.py +++ b/tests/test_particles.py @@ -1,7 +1,11 @@ import numpy as np import pytest +from giant.data.loader import TopNMap from giant.particles import ( + decode_embedding_nearest, + decode_topn_class, + invert_dense_map, nearest_known_pdg, particle_mass_charge, particle_phys_array, @@ -126,3 +130,101 @@ def test_nearest_known_pdg_shape(): ) assert result.shape == (n,) assert set(result.tolist()) <= set(candidates) + + +# ── invert_dense_map ───────────────────────────────────────────────────── + + +def test_invert_dense_map_round_trips(): + pdg_map = {22: 0, 11: 1, -11: 2, 2212: 3} + inv = invert_dense_map(pdg_map) + for pdg, idx in pdg_map.items(): + assert inv[idx] == pdg + + +# ── decode_topn_class ──────────────────────────────────────────────────── + + +def _topn_fixture(): + # n_classes=4: photon/electron/positron get their own class (0,1,2), + # everything else (proton, neutron) falls into "other" (class 3). + class_map = {22: 0, 11: 1, -11: 2, 2212: 3, 2112: 3} + other_members = {2212: 7, 2112: 3} + return TopNMap(class_map=class_map, other_members=other_members), 4 + + +def test_decode_topn_class_known_classes_are_exact(): + topn_map, n_classes = _topn_fixture() + out = decode_topn_class(np.array([0, 1, 2]), topn_map, n_classes) + np.testing.assert_array_equal(out, [22, 11, -11]) + + +def test_decode_topn_class_other_modal_picks_most_frequent(): + topn_map, n_classes = _topn_fixture() + out = decode_topn_class(np.array([3, 3]), topn_map, n_classes, other_policy="modal") + assert (out == 2212).all() # count 7 > 3 + + +def test_decode_topn_class_other_drop_returns_zero_sentinel(): + topn_map, n_classes = _topn_fixture() + out = decode_topn_class(np.array([3]), topn_map, n_classes, other_policy="drop") + assert out[0] == 0 + + +def test_decode_topn_class_other_sample_stays_within_members(): + topn_map, n_classes = _topn_fixture() + rng = np.random.default_rng(0) + out = decode_topn_class( + np.full(50, 3), topn_map, n_classes, other_policy="sample", rng=rng + ) + assert set(out.tolist()) <= {2212, 2112} + + +def test_decode_topn_class_unknown_other_policy_raises(): + topn_map, n_classes = _topn_fixture() + with pytest.raises(ValueError): + decode_topn_class(np.array([3]), topn_map, n_classes, other_policy="bogus") + + +def test_decode_topn_class_empty_other_members_raises(): + class_map = {22: 0, 11: 1} + topn_map = TopNMap(class_map=class_map, other_members={}) + with pytest.raises(ValueError): + decode_topn_class(np.array([1]), topn_map, 2, other_policy="sample") + + +def test_decode_topn_class_preserves_shape(): + topn_map, n_classes = _topn_fixture() + idx = np.array([[0, 1], [2, 3]]) + out = decode_topn_class(idx, topn_map, n_classes, other_policy="modal") + assert out.shape == (2, 2) + + +# ── decode_embedding_nearest ───────────────────────────────────────────── + + +def test_decode_embedding_nearest_exact_row_recovers_pdg(): + emb_weight = np.array([[1.0, 0.0], [0.0, 1.0], [-1.0, -1.0]]) + idx_to_pdg = {0: 22, 1: 11, 2: 2212} + vectors = np.array([[0.0, 1.0], [-1.0, -1.0]]) # exact rows 1, 2 + pdg, dist = decode_embedding_nearest(vectors, emb_weight, idx_to_pdg) + np.testing.assert_array_equal(pdg, [11, 2212]) + np.testing.assert_allclose(dist, [0.0, 0.0], atol=1e-8) + + +def test_decode_embedding_nearest_off_manifold_snaps_to_closest_row(): + emb_weight = np.array([[1.0, 0.0], [0.0, 1.0]]) + idx_to_pdg = {0: 22, 1: 11} + vectors = np.array([[0.9, 0.2]]) # closer to row 0 + pdg, dist = decode_embedding_nearest(vectors, emb_weight, idx_to_pdg) + assert pdg[0] == 22 + assert dist[0] > 0.0 + + +def test_decode_embedding_nearest_preserves_leading_shape(): + emb_weight = np.array([[1.0, 0.0], [0.0, 1.0]]) + idx_to_pdg = {0: 22, 1: 11} + vectors = np.random.default_rng(0).standard_normal((3, 4, 2)) + pdg, dist = decode_embedding_nearest(vectors, emb_weight, idx_to_pdg) + assert pdg.shape == (3, 4) + assert dist.shape == (3, 4) diff --git a/tests/test_phase2.py b/tests/test_phase2.py index f548f7a..afb12bd 100644 --- a/tests/test_phase2.py +++ b/tests/test_phase2.py @@ -19,13 +19,6 @@ from giant.model.schedule import ( ) from giant.sample import sample_secondaries -_SAMPLE_SECONDARIES_XFAIL_REASON = ( - "giant/sample.py isn't updated yet — sample_secondaries calls the " - "decoder positionally as decoder(x, t, cond_cont, cond_cat, stage1_out), " - "which doesn't match Stage2OneShot's new forward signature. Deferred to " - "docs/v0.3.0-design.md step 6." -) - # ── helpers ────────────────────────────────────────────────────────────────── @@ -300,7 +293,6 @@ def test_flow_matching_loss_secondary_ar_has_grad(): # ── sampling ────────────────────────────────────────────────────────────────── -@pytest.mark.xfail(reason=_SAMPLE_SECONDARIES_XFAIL_REASON, strict=False) def test_sample_secondaries_shapes(): B, pdg, mat = 6, 3, 2 decoder = _sec_decoder(pdg, mat) @@ -316,7 +308,6 @@ def test_sample_secondaries_shapes(): assert sec_valid.dtype == torch.bool -@pytest.mark.xfail(reason=_SAMPLE_SECONDARIES_XFAIL_REASON, strict=False) def test_sample_secondaries_valid_mask_matches_n_sec(): B, pdg, mat = 4, 3, 2 decoder = _sec_decoder(pdg, mat) diff --git a/tests/test_rollout.py b/tests/test_rollout.py index 0e71b90..f03eab8 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -9,23 +9,19 @@ import pytest import torch from giant.constants import TERM_ESCAPED, TERM_MAX_STEPS, TERM_UNKNOWN_PDG, K_MAX +from giant.data.loader import TopNMap from giant.data.transforms import Normalizer -from giant.model.network import Stage1Model, Stage2OneShot -from giant.rollout import make_seed_frontier, rollout +from giant.model.network import ( + Stage1Model, + Stage2Autoregressive, + Stage2OneShot, + stage2_trunk_sec_dim, +) +from giant.rollout import L1DistCollector, make_seed_frontier, rollout pytest.importorskip("sklearn") from giant import geometry as g # noqa: E402 -# giant/rollout.py isn't updated yet — it drives Stage1Model/Stage2OneShot -# through giant.sample's sample_flow/sample_secondaries, which still call -# models with the pre-refactor positional convention -# (model(x, t, cond_cont, cond_cat)) that no longer matches these classes' -# forward signatures. Deferred to docs/v0.3.0-design.md step 6/§10. -pytestmark = pytest.mark.xfail( - reason="giant/rollout.py not updated for v0.3.0 network.py yet (step 6)", - strict=False, -) - PDG_MAP = {22: 0, 11: 1, -11: 2} MAT_MAP = {"G4_AIR": 0, "G4_PbWO4": 1} @@ -350,3 +346,248 @@ def test_on_chunk_never_buffers_full_records(): assert rec.termination_reason_counts == {"natural_end": 1} with pytest.raises(AssertionError): rec.to_dict() + + +# ── v0.3.0 step 6: per-stage generators, AR decoder, particle_type.target ─── + +# emb_dim=3: "other" (class idx 2) is shared by -11 and 13 (muon), matching +# the real shape build_pdg_topn_map_from_files produces — see +# decode_topn_class's docstring. +PDG_TOPN_MAP = TopNMap( + class_map={22: 0, 11: 1, -11: 2, 13: 2}, + other_members={-11: 5, 13: 1}, +) + + +def _models_v3( + conditioning="physical", + decoder="one_shot", + target="physical", + generator1="flow", + generator2="flow", + k_max=6, + emb_dim=4, + stage2_has_n_sec_head=True, +): + particle_cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1} + material_cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1} + # A fresh v0.3.0 Stage1Model — no n_sec_head_k_max, unlike _models() above + # (decision 1 moves n_sec ownership to stage 2 by default). + s1 = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator1, + noise_dim=8, + ) + particle_type_cfg = {"target": target} + common = dict( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator2, + time_dim=16, + noise_dim=8, + k_max=k_max, + particle_type_cfg=particle_type_cfg, + build_n_sec_head=stage2_has_n_sec_head, + ) + if decoder == "one_shot": + sec_dim = stage2_trunk_sec_dim(particle_type_cfg, generator2, k_max, emb_dim) + s2 = Stage2OneShot(sec_dim=sec_dim, **common) + else: + s2 = Stage2Autoregressive(**common) + return s1.eval(), s2.eval() + + +def _run_v3( + s1, + s2, + escape_threshold=1e9, + energy_cutoff=1.0, + max_steps=15, + max_tracks_per_event=100, + seeds=None, + conditioning="physical", + pdg_topn_map=None, + other_policy="sample", + seed=0, + stage1_ddpm_steps=1000, + l1_dist_collector=None, +): + torch.manual_seed(0) + np.random.seed(0) + cond, tgt, sec_phys = _norms() + return rollout( + s1, + s2, + _oracle(), + seeds or _seeds(), + cond, + tgt, + sec_phys, + PDG_MAP, + MAT_MAP, + energy_cutoff=energy_cutoff, + max_steps=max_steps, + steps=3, + batch_size=128, + max_tracks_per_event=max_tracks_per_event, + escape_threshold=escape_threshold, + conditioning=conditioning, + pdg_topn_map=pdg_topn_map, + other_policy=other_policy, + seed=seed, + stage1_ddpm_steps=stage1_ddpm_steps, + l1_dist_collector=l1_dist_collector, + ) + + +def test_rollout_stage2_owns_n_sec_when_stage1_has_no_head(fake_material_props): + """A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1) — n_sec + must come from Stage2's own head instead, and the run must still + complete and conserve energy.""" + s1, s2 = _models_v3() + rec = _run_v3(s1, s2) + assert len(rec["event_id"]) > 0 + seeds = _seeds() + for i, ev in enumerate(seeds["event_id"]): + m = rec["event_id"] == ev + dep = rec["edep"][m].sum() + leak = rec["pre_E"][m & (rec["termination_reason"] == TERM_ESCAPED)].sum() + assert dep + leak == pytest.approx(seeds["pre_E"][i], rel=1e-4) + + +def test_resolve_n_sec_raises_when_neither_stage_owns_head(fake_material_props): + """Neither stage owning n_sec_head only happens for a + stage2_model.n_sec.mode other than "head" — not a valid rollout-capable + checkpoint, and must fail with a clear error rather than crash deep + inside predict_n_sec.""" + s1, s2 = _models_v3(stage2_has_n_sec_head=False) + with pytest.raises(RuntimeError, match="n_sec_head"): + _run_v3(s1, s2) + + +@pytest.mark.parametrize("decoder", ["one_shot", "autoregressive"]) +@pytest.mark.parametrize("generator2", ["flow", "wgan"]) +def test_rollout_physical_target_decoder_generator_matrix( + fake_material_props, decoder, generator2 +): + """Every (decoder, stage2 generator) combination under + particle_type.target="physical" must run to completion and conserve + energy — the matrix docs/v0.3.0-design.md §7 calls out for comparison.""" + s1, s2 = _models_v3(decoder=decoder, generator2=generator2) + rec = _run_v3(s1, s2) + assert len(rec["event_id"]) > 0 + seeds = _seeds() + for i, ev in enumerate(seeds["event_id"]): + m = rec["event_id"] == ev + dep = rec["edep"][m].sum() + leak = rec["pre_E"][m & (rec["termination_reason"] == TERM_ESCAPED)].sum() + assert dep + leak == pytest.approx(seeds["pre_E"][i], rel=1e-4) + + +def test_sample_stage1_dispatches_ddpm_by_generator_kind(): + """stage1_model.generator="ddpm" must be dispatched to sample_ddpm + (previously _step_chunk silently fell through to the flow ODE sampler + regardless of the checkpoint's actual generator — see giant.sample.sample_stage1). + A short T avoids the reverse-diffusion numerical blowup an untrained, + random-weight network produces over many steps; that instability is a + property of sampling from an untrained net, not of the dispatch logic + under test here, so a full oracle-driven rollout isn't needed.""" + from giant.sample import sample_stage1 + + s1, _ = _models_v3(generator1="ddpm") + cond_cont = torch.randn(6, 15) + cond_cat = torch.zeros(6, 2, dtype=torch.long) + sample, n_sec = sample_stage1(s1, cond_cont, cond_cat, steps=10, ddpm_steps=5) + assert sample.shape == (6, 9) + assert n_sec is None # fresh v0.3.0 Stage1Model owns no n_sec_head + + +@pytest.mark.parametrize("decoder", ["one_shot", "autoregressive"]) +def test_rollout_onehot_target_end_to_end(fake_material_props, decoder): + """particle_type.target="onehot" resolves a concrete PDG via + decode_topn_class (argmax + other_policy), and that PDG's real physics + (giant.particles.particle_phys_array) become the secondary's identity — + unlike "physical", not just a reporting label.""" + s1, s2 = _models_v3(decoder=decoder, target="onehot", emb_dim=3) + rec = _run_v3(s1, s2, pdg_topn_map=PDG_TOPN_MAP, other_policy="modal") + assert len(rec["event_id"]) > 0 + # Every spawned secondary's nominal pdg must be one decode_topn_class can + # actually produce (the topn map's known classes + its "other" members). + possible = set(PDG_TOPN_MAP.class_map.keys()) | set( + PDG_TOPN_MAP.other_members.keys() + ) + secondary_pdgs = set(rec["pdg"][rec["generation"] > 0].tolist()) + assert secondary_pdgs <= possible + + +def test_rollout_onehot_target_missing_topn_map_raises(fake_material_props): + s1, s2 = _models_v3(target="onehot", emb_dim=3) + with pytest.raises(RuntimeError, match="pdg_topn_map"): + _run_v3(s1, s2, pdg_topn_map=None) + + +@pytest.mark.parametrize("decoder", ["one_shot", "autoregressive"]) +def test_rollout_embedding_target_end_to_end(decoder): + """particle_type.target="embedding" L1-snaps to the nearest row of the + conditioning's own particle embedding table, so every resolved PDG must + be a real member of the dense training vocab (pdg_map) — unlike + "onehot", there is no "other" bucket to fall outside of.""" + s1, s2 = _models_v3( + conditioning="embedding", decoder=decoder, target="embedding", emb_dim=4 + ) + rec = _run_v3(s1, s2, conditioning="embedding") + assert len(rec["event_id"]) > 0 + secondary_pdgs = set(rec["pdg"][rec["generation"] > 0].tolist()) + assert secondary_pdgs <= set(PDG_MAP.keys()) + + +def test_l1_dist_collector_populated_only_for_embedding_target(): + """§11.3: the L1-distance diagnostic only makes sense under + particle_type.target="embedding" — a physical-target run must leave the + collector empty rather than silently accumulating garbage.""" + s1, s2 = _models_v3(target="physical") + collector = L1DistCollector() + _run_v3(s1, s2, l1_dist_collector=collector) + assert collector.n == 0 + assert collector.summary() is None + + +def test_l1_dist_collector_accumulates_for_embedding_target(): + s1, s2 = _models_v3(conditioning="embedding", target="embedding", emb_dim=4) + collector = L1DistCollector() + rec = _run_v3(s1, s2, conditioning="embedding", l1_dist_collector=collector) + n_secondaries = int((rec["generation"] > 0).sum()) + assert n_secondaries > 0 # sanity: the tiny model does spawn secondaries + summary = collector.summary() + assert summary is not None + assert summary["n"] == collector.n > 0 + assert summary["min"] <= summary["mean"] <= summary["max"] + assert summary["std"] >= 0.0 + assert len(summary["hist_edges"]) == len(summary["hist_counts"]) + 1 + assert sum(summary["hist_counts"]) <= summary["n"] # some may fall outside [lo, hi) + + +def test_l1_dist_collector_add_ignores_invalid_slots(): + collector = L1DistCollector() + dist = np.array([[1.0, 5.0, 9.0]]) + valid = np.array([[True, False, True]]) + collector.add(dist, valid) + assert collector.n == 2 + assert collector.minimum == 1.0 + assert collector.maximum == 9.0 + + +def test_l1_dist_collector_add_empty_is_noop(): + collector = L1DistCollector() + collector.add(np.zeros((0, 3)), np.zeros((0, 3), dtype=bool)) + assert collector.n == 0 + assert collector.summary() is None diff --git a/tests/test_router.py b/tests/test_router.py index 96b8c5b..8d791d7 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -869,21 +869,17 @@ def test_build_models_routed_stage2_ties_to_stage1_router(): assert stage1.trunk.router is stage2.trunk.router -@pytest.mark.xfail( - reason=( - "giant/sample.py isn't updated yet — its sample_flow/sample_secondaries " - "call models positionally as model(x, t, cond_cont, cond_cat), which " - "doesn't match Stage1Model/Stage2OneShot's new forward signature. " - "Deferred to docs/v0.3.0-design.md step 6." - ), - strict=False, -) def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): from giant.sample import sample_flow, sample_secondaries cfg = _nested_cfg( pdg_vocab=3, mat_vocab=2, + # PdgRouter (axis1) always builds its own training-vocab embedding, + # incompatible with conditioning.particle.type="physical" (the + # _nested_cfg default) — see _check_router_conditioning_compat. + particle_type="embedding", + material_type="embedding", stage1_router={ "enabled": True, "type": "composed", @@ -900,6 +896,11 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) assert stage1_norm.shape == (B, X_DIM) + # A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1 moves it to + # stage 2) — sample_flow returns n_sec_pred=None here, and n_sec must be + # asked of stage2 instead, using the just-sampled stage1_norm as context. + assert n_sec_pred is None + n_sec_pred = stage2.predict_n_sec(cond_cont, cond_cat, stage1_norm).argmax(dim=-1) assert n_sec_pred.shape == (B,) sec_cont, sec_type_emb, sec_valid = sample_secondaries( @@ -1075,15 +1076,6 @@ def test_build_models_routed_when_enabled(): assert len(stage2.trunk.experts) == 4 -@pytest.mark.xfail( - reason=( - "giant/sample.py isn't updated yet — its sample_flow/sample_secondaries " - "call models positionally as model(x, t, cond_cont, cond_cat), which " - "doesn't match Stage1Model/Stage2OneShot's new forward signature. " - "Deferred to docs/v0.3.0-design.md step 6." - ), - strict=False, -) def test_build_models_routed_pair_is_drop_in_for_sample_flow(): """Exercise the exact calling convention giant/sample.py uses.""" from giant.sample import sample_flow, sample_secondaries @@ -1100,6 +1092,11 @@ def test_build_models_routed_pair_is_drop_in_for_sample_flow(): cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) assert stage1_norm.shape == (B, X_DIM) + # A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1 moves it to + # stage 2) — sample_flow returns n_sec_pred=None here, and n_sec must be + # asked of stage2 instead, using the just-sampled stage1_norm as context. + assert n_sec_pred is None + n_sec_pred = stage2.predict_n_sec(cond_cont, cond_cat, stage1_norm).argmax(dim=-1) assert n_sec_pred.shape == (B,) sec_cont, sec_type_emb, sec_valid = sample_secondaries( diff --git a/tests/test_sample.py b/tests/test_sample.py new file mode 100644 index 0000000..670942a --- /dev/null +++ b/tests/test_sample.py @@ -0,0 +1,237 @@ +"""Tests for giant/sample.py's v0.3.0 stage-model sampling — the AR loop +(`sample_secondaries_ar`) and non-"physical" `particle_type.target` coverage +for the one-shot samplers (docs/v0.3.0-design.md step 6).""" + +import pytest +import torch + +from giant.constants import COND_DIM, CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM, X_DIM +from giant.model.network import ( + Stage1Model, + Stage2Autoregressive, + Stage2OneShot, + stage2_trunk_sec_dim, +) +from giant.sample import ( + sample_flow, + sample_secondaries, + sample_secondaries_ar, + sample_secondaries_wgan, + sample_wgan, +) + +_PHYS_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} + + +def _particle_material_cfg(conditioning: str, emb_dim: int) -> tuple[dict, dict]: + cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1} + return dict(cfg), dict(cfg) + + +def _cond(B: int, pdg: int = 3, mat: int = 2) -> tuple[torch.Tensor, torch.Tensor]: + cond_cont = torch.randn(B, COND_DIM) + cond_cat = torch.stack( + [torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1 + ) + return cond_cont, cond_cat + + +def _conditioning_for(target: str) -> str: + # target="embedding" regresses against the conditioning's own embedding + # table (docs/v0.3.0-design.md §3.3) — only meaningful when the + # conditioning axis is itself "embedding". + return "embedding" if target == "embedding" else "physical" + + +def _stage2_oneshot( + target: str, generator: str, emb_dim: int = 6, pdg: int = 3, mat: int = 2 +) -> Stage2OneShot: + particle_cfg, material_cfg = _particle_material_cfg( + _conditioning_for(target), emb_dim + ) + particle_type_cfg = {"target": target} + # build_models (giant/model/network.py) computes sec_dim this same way + # before constructing Stage2OneShot — its own default (SEC_DIM, the + # "physical" width) is only correct for target="physical". + sec_dim = stage2_trunk_sec_dim(particle_type_cfg, generator, K_MAX, emb_dim) + return Stage2OneShot( + pdg_vocab=pdg, + mat_vocab=mat, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator, + time_dim=16, + noise_dim=8, + sec_dim=sec_dim, + particle_type_cfg=particle_type_cfg, + ).eval() + + +def _stage2_ar( + target: str, + generator: str, + emb_dim: int = 6, + pdg: int = 3, + mat: int = 2, + k_max: int = 5, +) -> Stage2Autoregressive: + particle_cfg, material_cfg = _particle_material_cfg( + _conditioning_for(target), emb_dim + ) + return Stage2Autoregressive( + pdg_vocab=pdg, + mat_vocab=mat, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator, + time_dim=16, + noise_dim=8, + k_max=k_max, + particle_type_cfg={"target": target}, + ).eval() + + +def _expected_type_dim(target: str, emb_dim: int) -> int: + return PARTICLE_PHYS_DIM if target == "physical" else emb_dim + + +# ── Stage-1 n_sec ownership (decision 1) ──────────────────────────────────── + + +def test_sample_flow_returns_none_n_sec_when_stage1_owns_no_head(): + model = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=_PHYS_CFG, + material_cfg=_PHYS_CFG, + hidden_dim=16, + n_res_blocks=1, + ) + cond_cont, cond_cat = _cond(4) + sample, n_sec = sample_flow(model, cond_cont, cond_cat, steps=2) + assert sample.shape == (4, X_DIM) + assert n_sec is None + + +def test_sample_wgan_returns_none_n_sec_when_stage1_owns_no_head(): + model = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=_PHYS_CFG, + material_cfg=_PHYS_CFG, + hidden_dim=16, + n_res_blocks=1, + generator="wgan", + noise_dim=8, + ) + cond_cont, cond_cat = _cond(4) + sample, n_sec = sample_wgan(model, cond_cont, cond_cat) + assert sample.shape == (4, X_DIM) + assert n_sec is None + + +def test_sample_flow_returns_n_sec_for_legacy_stage1(): + model = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=_PHYS_CFG, + material_cfg=_PHYS_CFG, + hidden_dim=16, + n_res_blocks=1, + n_sec_head_k_max=K_MAX, + ) + cond_cont, cond_cat = _cond(5) + _, n_sec = sample_flow(model, cond_cont, cond_cat, steps=2) + assert n_sec.shape == (5,) + + +# ── Stage2OneShot: non-"physical" particle_type.target ────────────────────── + + +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +def test_sample_secondaries_flow_shapes_by_target(target): + B, emb_dim = 5, 6 + decoder = _stage2_oneshot(target, "flow", emb_dim=emb_dim) + cond_cont, cond_cat = _cond(B) + stage1_out = torch.randn(B, X_DIM) + n_sec_pred = torch.randint(0, K_MAX + 1, (B,)) + sec_cont, sec_type, sec_valid = sample_secondaries( + decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 + ) + assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM) + assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim)) + assert sec_valid.shape == (B, K_MAX) + assert torch.isfinite(sec_cont).all() + assert torch.isfinite(sec_type).all() + + +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +def test_sample_secondaries_wgan_shapes_by_target(target): + B, emb_dim = 5, 6 + decoder = _stage2_oneshot(target, "wgan", emb_dim=emb_dim) + cond_cont, cond_cat = _cond(B) + stage1_out = torch.randn(B, X_DIM) + n_sec_pred = torch.randint(0, K_MAX + 1, (B,)) + sec_cont, sec_type, sec_valid = sample_secondaries_wgan( + decoder, cond_cont, cond_cat, stage1_out, n_sec_pred + ) + assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM) + assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim)) + assert sec_valid.shape == (B, K_MAX) + + +# ── Stage2Autoregressive ───────────────────────────────────────────────────── + + +@pytest.mark.parametrize("generator", ["flow", "wgan"]) +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +def test_sample_secondaries_ar_shapes(target, generator): + B, k_max, emb_dim = 4, 5, 6 + decoder = _stage2_ar(target, generator, emb_dim=emb_dim, k_max=k_max) + cond_cont, cond_cat = _cond(B) + stage1_out = torch.randn(B, X_DIM) + n_sec_pred = torch.randint(0, k_max + 1, (B,)) + sec_cont, sec_type, sec_valid = sample_secondaries_ar( + decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 + ) + assert sec_cont.shape == (B, k_max, CONT_SLOT_DIM) + assert sec_type.shape == (B, k_max, _expected_type_dim(target, emb_dim)) + assert sec_valid.shape == (B, k_max) + assert torch.isfinite(sec_cont).all() + assert torch.isfinite(sec_type).all() + + +@pytest.mark.parametrize("generator", ["flow", "wgan"]) +@pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) +def test_sample_secondaries_ar_valid_mask_matches_n_sec(target, generator): + B, k_max, emb_dim = 3, 5, 6 + decoder = _stage2_ar(target, generator, emb_dim=emb_dim, k_max=k_max) + cond_cont, cond_cat = _cond(B) + stage1_out = torch.randn(B, X_DIM) + n_sec_pred = torch.tensor([0, 2, k_max]) + _, _, sec_valid = sample_secondaries_ar( + decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 + ) + for i, n in enumerate(n_sec_pred.tolist()): + assert sec_valid[i, :n].all() + assert not sec_valid[i, n:].any() + + +def test_sample_secondaries_ar_first_slot_has_no_history(): + """Slot 0 always has has_prev=False internally — nothing to assert on + the public API directly, but a k_max=1 run should not crash on the + "previous token" path at all (has_prev never true).""" + B, emb_dim = 3, 6 + decoder = _stage2_ar("physical", "flow", emb_dim=emb_dim, k_max=1) + cond_cont, cond_cat = _cond(B) + stage1_out = torch.randn(B, X_DIM) + n_sec_pred = torch.tensor([0, 1, 1]) + sec_cont, sec_type, sec_valid = sample_secondaries_ar( + decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 + ) + assert sec_cont.shape == (B, 1, CONT_SLOT_DIM) + assert sec_valid.tolist() == [[False], [True], [True]] diff --git a/tests/test_type_embedding_distance.py b/tests/test_type_embedding_distance.py new file mode 100644 index 0000000..c17f965 --- /dev/null +++ b/tests/test_type_embedding_distance.py @@ -0,0 +1,43 @@ +"""Tests for the secondary-type embedding-distance diagnostic +(giant.analysis.type_embedding_distance) — the §11.3 diagnostic.""" + +from __future__ import annotations + +from giant.analysis.type_embedding_distance import compute_type_embedding_l1_distance + + +def _summary(n=100): + return { + "n": n, + "mean": 1.23, + "std": 0.45, + "min": 0.01, + "max": 9.87, + "hist_edges": [0.0, 1.0, 2.0, 3.0], + "hist_counts": [30, 40, 30], + } + + +def test_none_is_unavailable(): + r = compute_type_embedding_l1_distance(None) + assert r.kind == "unavailable" + assert r.id == "type_embedding_l1_distance" + assert r.payload["note"] + + +def test_summary_produces_single_hist(): + r = compute_type_embedding_l1_distance(_summary()) + assert r.kind == "single_hist" + assert r.id == "type_embedding_l1_distance" + assert r.payload["edges"] == [0.0, 1.0, 2.0, 3.0] + assert r.payload["rollout"] == [30, 40, 30] + assert r.payload["log_x"] is True + assert r.payload["log_y"] is True + assert "n=100" in r.payload["note"] + + +def test_single_hist_payload_shape_matches_render_contract(): + """_render_single (giant.analysis.render) requires len(rollout) == + len(edges) - 1.""" + r = compute_type_embedding_l1_distance(_summary()) + assert len(r.payload["rollout"]) == len(r.payload["edges"]) - 1 diff --git a/tests/test_validate.py b/tests/test_validate.py index 5c316fa..61fa72f 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -1,5 +1,4 @@ import numpy as np -import pytest import torch from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM @@ -49,15 +48,6 @@ def _zero_secondaries_loader(B=4, n_batches=2): return batches -@pytest.mark.xfail( - reason=( - "giant/validate.py isn't updated yet — it calls the stage models " - "(sample_secondaries et al.) with the old positional convention, " - "which doesn't match Stage1Model/Stage2OneShot's new forward " - "signature. Deferred to docs/v0.3.0-design.md step 6/§10." - ), - strict=False, -) def test_validate_marginals_all_zero_secondaries_returns_nan_phys_kl(monkeypatch): """If n_sec_pred collapses to 0 across the whole validated set (realistic during early/unstable training), phys_kl must degrade to NaN instead of -- 2.39.5 From 200c6d243b00024b587c9182fbe87b993e8eb5c0 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 7 Aug 2026 13:15:56 +0200 Subject: [PATCH 10/30] v0.3.0 step 7: AttentionHistory (KV-cached) + scheduled/never teacher forcing AttentionHistory (giant/model/network.py) adds causal self-attention over the emitted-secondary prefix as the alternative to MarkovHistory, with a parallel forward() for training and an init_cache()/step() KV-cache path for sample.py's per-slot AR inference loop, wired into Stage2Autoregressive via history="attention". giant/train.py adds _stage2_tf_prob and _assemble_stage2_ar_inputs_scheduled, mixing ground-truth history with a detached sample_secondaries_ar self-sample per slot so teacher_forcing="scheduled"/"never" close the train/inference gap teacher_forcing="always" always avoided; wired into both stage-2 AR trainers. config.py's validate_config no longer rejects these two previously unimplemented schema values. Co-Authored-By: Claude Sonnet 5 --- giant/config.py | 13 ++- giant/model/network.py | 199 ++++++++++++++++++++++++++++++++++++--- giant/sample.py | 28 +++++- giant/train.py | 209 ++++++++++++++++++++++++++++++++++++++--- tests/test_config.py | 32 ++++++- tests/test_network.py | 120 ++++++++++++++++++++++- tests/test_sample.py | 11 ++- tests/test_train.py | 95 +++++++++++++++++-- 8 files changed, 654 insertions(+), 53 deletions(-) diff --git a/giant/config.py b/giant/config.py index 93972e6..4357c70 100644 --- a/giant/config.py +++ b/giant/config.py @@ -735,18 +735,17 @@ def validate_config(cfg: dict) -> None: if _get_path(cfg, "stage2_model.decoder") == "autoregressive": history = _get_path(cfg, "stage2_model.autoregressive.history") - if history != "markov": + if history not in ("markov", "attention"): raise ValueError( - f"stage2_model.autoregressive.history = {history!r} is " - "accepted by the schema but not implemented until v0.3.0 " - "step 7 — use 'markov'" + f"stage2_model.autoregressive.history = {history!r} — must " + "be 'markov' or 'attention'" ) teacher_forcing = _get_path(cfg, "stage2_model.autoregressive.teacher_forcing") - if teacher_forcing != "always": + if teacher_forcing not in ("always", "scheduled", "never"): raise ValueError( "stage2_model.autoregressive.teacher_forcing = " - f"{teacher_forcing!r} is accepted by the schema but not " - "implemented until v0.3.0 step 7 — use 'always'" + f"{teacher_forcing!r} — must be 'always', 'scheduled' or " + "'never'" ) diff --git a/giant/model/network.py b/giant/model/network.py index 0624e13..a058af6 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -789,9 +789,16 @@ def build_trunk( class HistoryEncoder(nn.Module): """Interface for stage-2 autoregressive per-token history summaries: - `forward(feat, has_prev) -> (B, K, out_dim)`. `MarkovHistory` is the only - implementation until v0.3.0 step 7 (`AttentionHistory`, - `history = "attention"`).""" + `forward(feat, has_prev) -> (B, K, out_dim)`, a single parallel pass over + a full (teacher-forced) token sequence — used by training. `MarkovHistory` + and `AttentionHistory` (docs/v0.3.0-design.md §6.2) are the two + implementations. Inference (`giant/sample.py`) generates one token at a + time and cannot afford `forward`'s per-step cost to be O(K) (attention + would then be O(K^2) over a rollout's k_max loop); encoders that need + incremental state for that path additionally implement `init_cache`/ + `step` (see `AttentionHistory`) — `MarkovHistory` doesn't need to, since + its per-step cost is already O(1) (it only ever looks at the previous + token, not the full prefix).""" def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: raise NotImplementedError @@ -819,6 +826,126 @@ class MarkovHistory(HistoryEncoder): return self.mlp(x) +class _CausalAttnBlock(nn.Module): + """One pre-norm causal self-attention block for `AttentionHistory`. + + Exposes two forward paths that must agree (see + `test_attention_history_step_matches_forward` in `tests/test_network.py`): + `forward` — the full-sequence, causally-masked pass used for training; + `step` — an incremental pass for inference, given the *pre-attention* + normalized hidden states of every earlier position (`kv_cache`, i.e. + `norm1(x)` for positions `< t`, not `x` itself). Caching `norm1(x)` rather + than raw `x` is what makes `step` correct: this block's attention needs + exactly that quantity as keys/values, and `LayerNorm` has no cross-position + interaction, so recomputing it per position instead of caching it would + still be correct but pointlessly repeat work. The *next* block's cache is + built from a different sequence (this block's output), so each block owns + an independent cache entry. + """ + + def __init__(self, dim: int, n_heads: int, dropout: float = 0.0) -> None: + super().__init__() + self.norm1 = nn.LayerNorm(dim) + self.attn = nn.MultiheadAttention( + dim, n_heads, dropout=dropout, batch_first=True + ) + self.norm2 = nn.LayerNorm(dim) + self.mlp = nn.Sequential( + nn.Linear(dim, 4 * dim), nn.GELU(), nn.Linear(4 * dim, dim) + ) + + def forward(self, x: torch.Tensor, causal_mask: torch.Tensor) -> torch.Tensor: + h = self.norm1(x) + attn_out, _ = self.attn(h, h, h, attn_mask=causal_mask, need_weights=False) + x = x + attn_out + x = x + self.mlp(self.norm2(x)) + return x + + def step( + self, x_new: torch.Tensor, kv_cache: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + """`x_new`: `(B, 1, dim)`, this position's input. `kv_cache`: `None` + (first position) or `(B, T, dim)` — `norm1(x)` of every earlier + position at this same block. Returns `(out, new_kv_cache)`, `out` + being this position's block output (`(B, 1, dim)`, to feed the next + block's `step`), `new_kv_cache` the same cache extended by this + position (to reuse at this block's *next* `step` call).""" + h_new = self.norm1(x_new) + kv = h_new if kv_cache is None else torch.cat([kv_cache, h_new], dim=1) + attn_out, _ = self.attn(h_new, kv, kv, need_weights=False) + x = x_new + attn_out + x = x + self.mlp(self.norm2(x)) + return x, kv + + +class AttentionHistory(HistoryEncoder): + """Causal self-attention over the emitted-token prefix + (docs/v0.3.0-design.md §6.2) — the more expressive alternative to + `MarkovHistory`'s fixed previous-token-only summary. `feat`/`has_prev` + follow the same shifted-by-one convention `MarkovHistory` and + `Stage2Autoregressive._token_cond` use: `feat[:, i]` is token `i - 1`'s + own `(energy_fraction, direction, type_representation)`, with a learned + start vector substituted at `has_prev == False` positions (only slot 0 in + practice — see `giant.train._ar_has_prev`). Causal masking then makes + position `i`'s output a function of `feat[:, 1:i+1]` — i.e. tokens + `0..i-1` — exactly the prefix available when predicting token `i`. + + `forward` is the parallel training path (one pass over the whole + teacher-forced sequence); `init_cache`/`step` are the incremental + inference path `giant/sample.py` uses, one new token per call, to avoid + re-encoding the whole prefix from scratch every slot (docs/v0.3.0-design.md + §10's "KV cache" note) — `step` must be called exactly once per slot (its + cache-extension is not idempotent), so a slot's output must be reused for + every model call within that slot (`forward`'s ODE substeps, or a separate + `predict_type` call) rather than re-derived — see + `Stage2Autoregressive.history_step`. + """ + + def __init__( + self, in_dim: int, out_dim: int, n_heads: int = 4, n_layers: int = 2 + ) -> None: + super().__init__() + self.start = nn.Parameter(torch.zeros(in_dim)) + self.in_proj = nn.Linear(in_dim, out_dim) + self.blocks = nn.ModuleList( + [_CausalAttnBlock(out_dim, n_heads) for _ in range(n_layers)] + ) + + def _embed(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: + start = self.start.view(1, 1, -1).expand_as(feat) + x = torch.where(has_prev.unsqueeze(-1), feat, start) + return self.in_proj(x) + + def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: + B, K, _ = feat.shape + x = self._embed(feat, has_prev) + mask = nn.Transformer.generate_square_subsequent_mask(K, device=feat.device) + for block in self.blocks: + x = block(x, mask) + return x + + def init_cache(self) -> list[torch.Tensor | None]: + return [None for _ in self.blocks] + + def step( + self, + token_feat: torch.Tensor, + has_prev: torch.Tensor, + cache: list[torch.Tensor | None], + ) -> tuple[torch.Tensor, list[torch.Tensor | None]]: + """`token_feat`/`has_prev`: `(B, 1, in_dim)`/`(B, 1)` — the newest + token's own features (what would be `feat[:, k]` in `forward`). + Advances every block's cache by this position and returns this + position's output (`(B, 1, out_dim)`, the correct history summary for + the NEXT slot) plus the updated cache.""" + x = self._embed(token_feat, has_prev) + new_cache: list[torch.Tensor | None] = [] + for block, kv in zip(self.blocks, cache): + x, kv_new = block.step(x, kv) + new_cache.append(kv_new) + return x, new_cache + + # --------------------------------------------------------------------------- # Stage models (docs/v0.3.0-design.md §5.3) # --------------------------------------------------------------------------- @@ -1082,8 +1209,8 @@ class Stage2OneShot(nn.Module): class Stage2Autoregressive(nn.Module): """Emits secondaries one at a time in descending-energy order (docs/v0.3.0-design.md §6), instead of `Stage2OneShot`'s simultaneous - k_max-slot prediction. Only `history = "markov"` is implemented (v0.3.0 - step 5) — `history = "attention"` raises immediately at construction. + k_max-slot prediction. `history` selects `MarkovHistory` or + `AttentionHistory` (`attn_n_heads`/`attn_n_layers`, attention only). `teacher_forcing` handling lives entirely in the trainer (`giant/train.py`), since it only affects how training inputs are assembled, not this module's architecture. @@ -1122,13 +1249,16 @@ class Stage2Autoregressive(nn.Module): build_n_sec_head: bool = True, particle_type_cfg: dict | None = None, history: str = "markov", + attn_n_heads: int = 4, + attn_n_layers: int = 2, ) -> None: super().__init__() - if history != "markov": - raise NotImplementedError( - f"stage2_model.autoregressive.history={history!r} is not " - "implemented until v0.3.0 step 7 — use 'markov'" + if history not in ("markov", "attention"): + raise ValueError( + f"stage2_model.autoregressive.history={history!r} — must be " + "'markov' or 'attention'" ) + self.history_kind = history self.generator_kind = generator self.noise_dim = noise_dim self.k_max = k_max @@ -1149,7 +1279,14 @@ class Stage2Autoregressive(nn.Module): # width — there's no dedicated stage2_model.autoregressive key for # this, a reasonable default rather than a design-doc-specified value. history_dim = cond_out_dim - self.history_encoder = MarkovHistory(CONT_SLOT_DIM + self.type_dim, history_dim) + hist_in_dim = CONT_SLOT_DIM + self.type_dim + self.history_encoder: HistoryEncoder = ( + AttentionHistory( + hist_in_dim, history_dim, n_heads=attn_n_heads, n_layers=attn_n_layers + ) + if history == "attention" + else MarkovHistory(hist_in_dim, history_dim) + ) token_fuse_in = ( cond_out_dim + context_dim + history_dim + 2 ) # +2: remaining_frac, slot_idx @@ -1205,14 +1342,48 @@ class Stage2Autoregressive(nn.Module): has_prev: torch.Tensor, remaining_frac: torch.Tensor, slot_idx: torch.Tensor, + hist: torch.Tensor | None = None, ) -> torch.Tensor: + """`hist`, if given, overrides recomputing `self.history_encoder` + from `history_feat`/`has_prev` — the inference-time KV-cache path + (`Stage2Autoregressive.history_step`) precomputes it once per slot and + passes it in here so a slot's (possibly several) model calls — an ODE + loop's substeps, or a separate `predict_type` call — read the same + cached history instead of each re-deriving (and, under attention, + re-appending to the cache — see `AttentionHistory.step`'s docstring).""" K = history_feat.size(1) base = self.cond_enc(cond_cont, cond_cat).unsqueeze(1).expand(-1, K, -1) ctx = self.context_adapter(stage1_out).unsqueeze(1).expand(-1, K, -1) - hist = self.history_encoder(history_feat, has_prev) + if hist is None: + hist = self.history_encoder(history_feat, has_prev) scalars = torch.stack([remaining_frac, slot_idx], dim=-1) return self.token_fuse(torch.cat([base, ctx, hist, scalars], dim=-1)) + def init_history_cache(self): + """Inference-only incremental-decoding state for `self.history_encoder` + (`giant/sample.py`'s AR loop): `None` under `history="markov"` (its + per-step cost is already O(1) — see `HistoryEncoder`'s docstring), or + `AttentionHistory.init_cache()` under `history="attention"`.""" + if isinstance(self.history_encoder, AttentionHistory): + return self.history_encoder.init_cache() + return None + + def history_step( + self, token_feat: torch.Tensor, has_prev: torch.Tensor, cache + ) -> tuple[torch.Tensor, object]: + """One inference slot's worth of history encoding: advances `cache` + (from `init_history_cache`, or a previous `history_step` call) by + `token_feat`/`has_prev` (`(B, 1, ...)` — the just-emitted previous + token, same convention `giant.sample.sample_secondaries_ar` already + threads as `prev_repr`), and returns `(hist, new_cache)` — `hist` is + this slot's history summary (pass it as `_token_cond`'s `hist=` to + every model call made for this slot), `new_cache` is what to pass into + the *next* slot's `history_step`. Must be called exactly once per + slot — see `AttentionHistory.step`'s docstring.""" + if isinstance(self.history_encoder, AttentionHistory): + return self.history_encoder.step(token_feat, has_prev, cache) + return self.history_encoder(token_feat, has_prev), cache + def forward( self, x_t: torch.Tensor, @@ -1224,6 +1395,7 @@ class Stage2Autoregressive(nn.Module): remaining_frac: torch.Tensor, slot_idx: torch.Tensor, t: torch.Tensor | None = None, + hist: torch.Tensor | None = None, ) -> torch.Tensor: B, K = x_t.shape[0], x_t.shape[1] c_emb = self._token_cond( @@ -1234,6 +1406,7 @@ class Stage2Autoregressive(nn.Module): has_prev, remaining_frac, slot_idx, + hist=hist, ) if self.time_emb is not None: assert t is not None @@ -1268,6 +1441,7 @@ class Stage2Autoregressive(nn.Module): has_prev: torch.Tensor, remaining_frac: torch.Tensor, slot_idx: torch.Tensor, + hist: torch.Tensor | None = None, ) -> torch.Tensor: if self.type_head is None: raise RuntimeError( @@ -1285,6 +1459,7 @@ class Stage2Autoregressive(nn.Module): has_prev, remaining_frac, slot_idx, + hist=hist, ) B, K, _ = c_emb.shape return self.type_head(c_emb.reshape(B * K, -1)).view(B, K, self.type_dim) @@ -1571,6 +1746,8 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: build_n_sec_head=legacy_owner != "stage1", particle_type_cfg=particle_type_cfg, history=ar_cfg.get("history", "markov"), + attn_n_heads=ar_cfg.get("attn_n_heads", 4), + attn_n_layers=ar_cfg.get("attn_n_layers", 2), ) else: sec_dim = stage2_trunk_sec_dim( diff --git a/giant/sample.py b/giant/sample.py index f9ba019..2f8df2f 100644 --- a/giant/sample.py +++ b/giant/sample.py @@ -256,17 +256,28 @@ def sample_secondaries_ar( once, so a flow/ddpm AR run costs ~`k_max * steps` model calls per physics step. + Under `history="attention"` the history encoding is computed once per + slot via `Stage2Autoregressive.history_step` (a KV-cache append, §10) + rather than re-derived by every model call inside that slot — so an ODE + loop's `steps` substeps, and the separate `predict_type` call when the + type slice isn't folded into the trunk output, all reuse the SAME `hist` + tensor for a given `k`. Recomputing per call instead would be merely + wasteful under markov (its per-call cost is already O(1)) but wrong under + attention: `AttentionHistory.step` mutates the cache by appending, so + calling it more than once per slot would double-count that slot's own + (not-yet-existing) predecessor. + The free-running history feature stays UNSNAPPED (mirrors the established "no snapping" precedent for `particle_type.target = "physical"` secondaries feeding their own future conditioning): `"physical"` carries the raw (log_mass, charge) forward as-is; `"embedding"` carries the raw predicted vector as-is; `"onehot"` is the one exception — its history slot must be a probability-simplex-shaped - vector (that's what `MarkovHistory` was trained on, `_type_repr`'s - `F.one_hot` ground truth), so it's the hard one-hot of `argmax(logits)`, - not the raw logits themselves. Discretizing further, into a concrete PDG - code, only ever happens once — at secondary-spawn time in - `giant/rollout.py` — never inside this loop. + vector (that's what `MarkovHistory`/`AttentionHistory` were trained on, + `_type_repr`'s `F.one_hot` ground truth), so it's the hard one-hot of + `argmax(logits)`, not the raw logits themselves. Discretizing further, + into a concrete PDG code, only ever happens once — at secondary-spawn + time in `giant/rollout.py` — never inside this loop. Returns (sec_cont, sec_type, sec_valid) — same shapes/meaning as `sample_secondaries`/`sample_secondaries_wgan`'s (see @@ -291,6 +302,7 @@ def sample_secondaries_ar( # Running per-token state, threaded from one slot to the next. prev_repr = torch.zeros(B, CONT_SLOT_DIM + type_dim, device=device) remaining = torch.ones(B, device=device) + history_cache = sec_decoder.init_history_cache() for k in range(k_max): has_prev = torch.full((B, 1), k >= 1, dtype=torch.bool, device=device) @@ -299,6 +311,9 @@ def sample_secondaries_ar( slot_idx = torch.full( (B, 1), k / max(k_max - 1, 1), device=device, dtype=torch.float32 ) + hist, history_cache = sec_decoder.history_step( + history_feat, has_prev, history_cache + ) if generator == "wgan": z = torch.randn(B, 1, sec_decoder.noise_dim, device=device) @@ -311,6 +326,7 @@ def sample_secondaries_ar( has_prev, remaining_frac, slot_idx, + hist=hist, ) else: x = torch.randn(B, 1, token_dim, device=device) @@ -327,6 +343,7 @@ def sample_secondaries_ar( remaining_frac, slot_idx, t=t, + hist=hist, ) x = x + v * dt token = x @@ -344,6 +361,7 @@ def sample_secondaries_ar( has_prev, remaining_frac, slot_idx, + hist=hist, ).squeeze(1) sec_cont[:, k] = cont_k diff --git a/giant/train.py b/giant/train.py index a504056..6a3689c 100644 --- a/giant/train.py +++ b/giant/train.py @@ -27,6 +27,7 @@ from giant.model.schedule import ( flow_matching_loss_secondary_ar, ) from giant.model.wgan import gradient_penalty, generator_loss +from giant.sample import sample_secondaries_ar from giant.validate import validate_marginals _CATCHABLE_SIGNALS = (signal.SIGINT, signal.SIGTERM) @@ -246,6 +247,119 @@ def _assemble_stage2_ar_inputs( } +def _stage2_tf_prob( + mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int +) -> float: + """P(condition slot k+1 on the TRUE token k rather than the model's own + prediction), for the current epoch (docs/v0.3.0-design.md §3.3 + `stage2_model.autoregressive.teacher_forcing`). `"always"`/`"never"` are + the two degenerate constants; `"scheduled"` linearly interpolates + `p_start` (epoch 0) to `p_end` (the final epoch) — standard scheduled + sampling (Bengio et al. 2015).""" + if mode == "always": + return 1.0 + if mode == "never": + return 0.0 + frac = epoch / max(total_epochs - 1, 1) + frac = min(max(frac, 0.0), 1.0) + return p_start + (p_end - p_start) * frac + + +def _history_repr_from_ar_sample( + sec_cont_pred: torch.Tensor, + sec_type_pred: torch.Tensor, + particle_type_cfg: dict, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """`(fraction, direction, type_repr)` — the same triple `_type_repr` / + `_stick_fraction` derive from ground truth, but from a free-running + `sample_secondaries_ar` self-sample instead, so the two can be mixed + slot-by-slot under scheduled sampling (`_assemble_stage2_ar_inputs_scheduled`). + `target="onehot"` collapses the raw per-slot type logits to a hard + one-hot of `argmax` — `sample_secondaries_ar`'s own history convention + (see its docstring), matching what `MarkovHistory`/`AttentionHistory` + were trained on; the other two targets are already the right + representation.""" + fraction = torch.sigmoid(sec_cont_pred[..., 0]) + direction = sec_cont_pred[..., 1:4] + if particle_type_cfg.get("target", "physical") == "onehot": + type_dim = sec_type_pred.size(-1) + type_repr = F.one_hot(sec_type_pred.argmax(-1), num_classes=type_dim).float() + else: + type_repr = sec_type_pred + return fraction, direction, type_repr + + +def _assemble_stage2_ar_inputs_scheduled( + model: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_ctx: torch.Tensor, + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + n_sec: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, + p_tf: float, + sample_steps: int, +) -> dict[str, torch.Tensor]: + """Scheduled-sampling counterpart of `_assemble_stage2_ar_inputs` + (docs/v0.3.0-design.md §3.3 `teacher_forcing` = "scheduled"/"never"): + each slot's history is the TRUE previous token with probability `p_tf` + (an independent per-example, per-slot Bernoulli draw) and the model's own + free-running prediction otherwise — closing the train/inference gap that + `teacher_forcing="always"` (ground truth throughout training) never sees. + `p_tf >= 1.0` degenerates exactly to `_assemble_stage2_ar_inputs` (and + skips self-sampling entirely), so callers can call this unconditionally. + + The free-running estimate is a REAL autoregressive self-sample — + `giant.sample.sample_secondaries_ar` under `torch.no_grad()` — not a + cheap one-step proxy, so building it costs the same `k_max` (`* steps` + for flow) sequential forwards `sample.py` pays at inference, EVERY batch + this is called on (§6.4's cost note, paid at train time too whenever + teacher_forcing != "always"). Fully detached: gradient only ever flows + through the "real" target path each stage trainer already uses + (`_assemble_stage2_ar_target`), never through this self-sample. + """ + device = sec_cont.device + B, K = sec_cont.shape[0], sec_cont.shape[1] + if p_tf >= 1.0: + return _assemble_stage2_ar_inputs( + sec_cont, sec_type_idx, particle_type_cfg, cond_enc, emb_dim + ) + + was_training = model.training + sec_cont_pred, sec_type_pred, _ = sample_secondaries_ar( + model, cond_cont, cond_cat, stage1_ctx, n_sec, steps=sample_steps + ) + if was_training: + model.train() + + fraction_gt = _stick_fraction(sec_cont) + dir_gt = sec_cont[..., 1:4] + type_repr_gt = _type_repr( + sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim + ) + fraction_pred, dir_pred, type_repr_pred = _history_repr_from_ar_sample( + sec_cont_pred, sec_type_pred, particle_type_cfg + ) + + use_gt = torch.rand(B, K, device=device) < p_tf + fraction = torch.where(use_gt, fraction_gt, fraction_pred) + direction = torch.where(use_gt.unsqueeze(-1), dir_gt, dir_pred) + type_repr = torch.where(use_gt.unsqueeze(-1), type_repr_gt, type_repr_pred) + + own_feat = torch.cat([fraction.unsqueeze(-1), direction, type_repr], dim=-1) + return { + "history_feat": _shift_prev(own_feat), + "has_prev": _ar_has_prev(K, device).expand(B, -1), + "remaining_frac": _remaining_energy_fraction(fraction), + "slot_idx": (torch.arange(K, device=device).float() / max(K - 1, 1)) + .unsqueeze(0) + .expand(B, -1), + } + + def _relax_onehot_type_slice( x_flat: torch.Tensor, k_max: int, @@ -362,6 +476,10 @@ class FlowDDPMStageTrainer(StageTrainer): particle_type_cfg: dict | None = None, particle_type_emb_dim: int = 16, decoder: str = "one_shot", + teacher_forcing: str = "always", + tf_p_start: float = 1.0, + tf_p_end: float = 1.0, + ar_sample_steps: int = 10, ) -> None: if is_stage2 and generator not in ("flow",): raise NotImplementedError( @@ -373,6 +491,12 @@ class FlowDDPMStageTrainer(StageTrainer): self.is_stage2 = is_stage2 self.generator = generator self.decoder = decoder + self.teacher_forcing = teacher_forcing + self.tf_p_start = tf_p_start + self.tf_p_end = tf_p_end + self.ar_sample_steps = ar_sample_steps + self.total_epochs = epochs + self.steps_per_epoch = max(steps_per_epoch, 1) self.device = device self.model = model.to(device) self.lambda_weight = lambda_weight @@ -513,7 +637,13 @@ class FlowDDPMStageTrainer(StageTrainer): l_type = (se * mask).sum() / denom return l_type, type_acc - def _compute(self, batch: tuple, device: torch.device) -> dict: + def _compute( + self, batch: tuple, device: torch.device, epoch: int | None = None + ) -> dict: + """`epoch=None` (the `val_loss` path) always uses full teacher + forcing (`p_tf=1.0`) regardless of `self.teacher_forcing` — validation + should stay a stable, non-stochastic ground-truth comparison; only + the training `step` path schedules `p_tf` by epoch.""" ( cond_cont, cond_cat, @@ -529,12 +659,30 @@ class FlowDDPMStageTrainer(StageTrainer): x1_s2 = None ar_inputs = None if self.is_stage2 and self.decoder == "autoregressive": - ar_inputs = _assemble_stage2_ar_inputs( + p_tf = ( + 1.0 + if epoch is None + else _stage2_tf_prob( + self.teacher_forcing, + self.tf_p_start, + self.tf_p_end, + epoch, + self.total_epochs, + ) + ) + ar_inputs = _assemble_stage2_ar_inputs_scheduled( + self.model, + cond_cont, + cond_cat, + stage1_ctx, sec_cont, sec_type_idx, + n_sec, self.particle_type_cfg, self.model.cond_enc, self.particle_type_emb_dim, + p_tf, + self.ar_sample_steps, ) x1_s2 = _assemble_stage2_ar_target( sec_cont, @@ -613,7 +761,8 @@ class FlowDDPMStageTrainer(StageTrainer): self.gumbel_tau_start, self.gumbel_tau_end, ) - out = self._compute(batch, device) + epoch = global_step // self.steps_per_epoch + out = self._compute(batch, device, epoch=epoch) self.optimizer.zero_grad() out["total"].backward() grad_norm = torch.nn.utils.clip_grad_norm_(self.params, 1.0) @@ -719,10 +868,20 @@ class WGANStageTrainer(StageTrainer): type_gumbel_tau_start: float = 1.0, type_gumbel_tau_end: float = 0.1, decoder: str = "one_shot", + teacher_forcing: str = "always", + tf_p_start: float = 1.0, + tf_p_end: float = 1.0, + ar_sample_steps: int = 10, ) -> None: self.name = name self.is_stage2 = is_stage2 self.decoder = decoder + self.teacher_forcing = teacher_forcing + self.tf_p_start = tf_p_start + self.tf_p_end = tf_p_end + self.ar_sample_steps = ar_sample_steps + self.total_epochs = epochs + self.steps_per_epoch = max(steps_per_epoch, 1) self.device = device self.model = model.to(device) self.critic = critic.to(device) @@ -810,12 +969,27 @@ class WGANStageTrainer(StageTrainer): return self.critic(x, cond_cont, cond_cat, stage1_ctx) if self.decoder == "autoregressive": - ar = _assemble_stage2_ar_inputs( + epoch = global_step // self.steps_per_epoch + p_tf = _stage2_tf_prob( + self.teacher_forcing, + self.tf_p_start, + self.tf_p_end, + epoch, + self.total_epochs, + ) + ar = _assemble_stage2_ar_inputs_scheduled( + self.model, + cond_cont, + cond_cat, + stage1_ctx, sec_cont, sec_type_idx, + n_sec, self.particle_type_cfg, self.model.cond_enc, self.particle_type_emb_dim, + p_tf, + self.ar_sample_steps, ) real = ( _assemble_stage2_ar_target( @@ -1051,16 +1225,15 @@ def _build_stage_trainers( } particle_type_emb_dim = cfg["conditioning"]["particle"]["emb_dim"] decoder = stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot" - if is_stage2 and decoder == "autoregressive": - teacher_forcing = (stage_cfg.get("autoregressive") or {}).get( - "teacher_forcing", "always" - ) - if teacher_forcing != "always": - raise NotImplementedError( - "stage2_model.autoregressive.teacher_forcing=" - f"{teacher_forcing!r} is not implemented until v0.3.0 " - "step 7 — use 'always'" - ) + ar_cfg = (stage_cfg.get("autoregressive") or {}) if is_stage2 else {} + teacher_forcing = ar_cfg.get("teacher_forcing", "always") + tf_p_start = ar_cfg.get("tf_p_start", 1.0) + tf_p_end = ar_cfg.get("tf_p_end", 1.0) + # AR self-sampling under scheduled/never teacher forcing reuses + # train.validate_steps as its flow-matching ODE step count — no + # dedicated config key for this (docs/v0.3.0-design.md §3.3 lists + # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). + ar_sample_steps = t.get("validate_steps", 10) if generator == "wgan": critic = critics.get(name) @@ -1090,6 +1263,10 @@ def _build_stage_trainers( type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), decoder=decoder, + teacher_forcing=teacher_forcing, + tf_p_start=tf_p_start, + tf_p_end=tf_p_end, + ar_sample_steps=ar_sample_steps, ) else: ddpm_n_steps = stage_cfg.get("ddpm", {}).get("n_steps", 1000) @@ -1116,6 +1293,10 @@ def _build_stage_trainers( particle_type_cfg=particle_type_cfg, particle_type_emb_dim=particle_type_emb_dim, decoder=decoder, + teacher_forcing=teacher_forcing, + tf_p_start=tf_p_start, + tf_p_end=tf_p_end, + ar_sample_steps=ar_sample_steps, ) return trainers diff --git a/tests/test_config.py b/tests/test_config.py index 4fb9b00..b57b5cf 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -1,6 +1,8 @@ from datetime import datetime from pathlib import Path +import pytest + from giant import config as gconfig _CONFIGS_DIR = Path(__file__).resolve().parents[1] / "configs" @@ -645,13 +647,37 @@ def test_validate_config_ar_default_markov_always_passes(): gconfig.validate_config(cfg) # must not raise -def test_validate_config_ar_history_attention_not_implemented(): +def test_validate_config_ar_history_attention_passes(): + """v0.3.0 step 7 implements history='attention' — must not raise.""" cfg = _cfg_with( **{ "stage2_model.decoder": "autoregressive", "stage2_model.autoregressive.history": "attention", } ) + gconfig.validate_config(cfg) # must not raise + + +@pytest.mark.parametrize("teacher_forcing", ["scheduled", "never"]) +def test_validate_config_ar_teacher_forcing_scheduled_or_never_passes(teacher_forcing): + """v0.3.0 step 7 implements teacher_forcing in {'scheduled', 'never'} — + must not raise.""" + cfg = _cfg_with( + **{ + "stage2_model.decoder": "autoregressive", + "stage2_model.autoregressive.teacher_forcing": teacher_forcing, + } + ) + gconfig.validate_config(cfg) # must not raise + + +def test_validate_config_ar_history_invalid_value_rejected(): + cfg = _cfg_with( + **{ + "stage2_model.decoder": "autoregressive", + "stage2_model.autoregressive.history": "bogus", + } + ) try: gconfig.validate_config(cfg) assert False, "expected ValueError" @@ -659,11 +685,11 @@ def test_validate_config_ar_history_attention_not_implemented(): assert "history" in str(e) -def test_validate_config_ar_teacher_forcing_scheduled_not_implemented(): +def test_validate_config_ar_teacher_forcing_invalid_value_rejected(): cfg = _cfg_with( **{ "stage2_model.decoder": "autoregressive", - "stage2_model.autoregressive.teacher_forcing": "scheduled", + "stage2_model.autoregressive.teacher_forcing": "bogus", } ) try: diff --git a/tests/test_network.py b/tests/test_network.py index dde4bf9..f3c5293 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -2,6 +2,7 @@ import pytest import torch from giant.constants import CONT_SLOT_DIM, COND_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM from giant.model.network import ( + AttentionHistory, ConditionEncoder, MarkovHistory, SinusoidalEmbedding, @@ -323,6 +324,70 @@ def test_markov_history_uses_start_vector_when_no_prev(): assert torch.allclose(out_a[:, 1:], out_b[:, 1:]) +# --- AttentionHistory (docs/v0.3.0-design.md §6.2, v0.3.0 step 7) ---------- + + +def test_attention_history_shape(): + hist = AttentionHistory(in_dim=7, out_dim=12, n_heads=2, n_layers=2) + B, K = 3, 5 + feat = torch.randn(B, K, 7) + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + out = hist(feat, has_prev) + assert out.shape == (B, K, 12) + + +def test_attention_history_uses_start_vector_when_no_prev(): + hist = AttentionHistory(in_dim=4, out_dim=6, n_heads=2, n_layers=1) + B, K = 2, 3 + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + feat_a = torch.randn(B, K, 4) + feat_b = feat_a.clone() + feat_b[:, 0] = torch.randn(B, 4) * 100 + out_a = hist(feat_a, has_prev) + out_b = hist(feat_b, has_prev) + assert torch.allclose(out_a[:, 0], out_b[:, 0], atol=1e-5) + + +def test_attention_history_is_causal(): + """Position i's output must not depend on feat at positions > i — unlike + MarkovHistory (which only ever looks at position i itself, already + trivially "causal"), this is AttentionHistory's actual contribution: + seeing the full prefix 0..i-1, never anything later.""" + hist = AttentionHistory(in_dim=4, out_dim=6, n_heads=2, n_layers=2) + hist.eval() + B, K = 2, 5 + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + feat_a = torch.randn(B, K, 4) + feat_b = feat_a.clone() + feat_b[:, 3:] = torch.randn(B, K - 3, 4) * 100 + with torch.no_grad(): + out_a = hist(feat_a, has_prev) + out_b = hist(feat_b, has_prev) + assert torch.allclose(out_a[:, :3], out_b[:, :3], atol=1e-5) + + +def test_attention_history_step_matches_forward(): + """The incremental KV-cache path (`init_cache`/`step`, + `giant/sample.py`'s AR loop) must reproduce `forward`'s parallel-pass + output exactly, one position at a time.""" + hist = AttentionHistory(in_dim=4, out_dim=6, n_heads=2, n_layers=2) + hist.eval() + B, K = 3, 6 + has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + feat = torch.randn(B, K, 4) + with torch.no_grad(): + expected = hist(feat, has_prev) + + cache = hist.init_cache() + outs = [] + for k in range(K): + out_k, cache = hist.step(feat[:, k : k + 1], has_prev[:, k : k + 1], cache) + outs.append(out_k) + stepped = torch.cat(outs, dim=1) + + assert torch.allclose(stepped, expected, atol=1e-5) + + # --- Stage2Autoregressive (docs/v0.3.0-design.md §6, v0.3.0 step 5) --------- @@ -361,16 +426,19 @@ def _ar_inputs(B: int, K: int, hist_dim: int): return history_feat, has_prev, remaining_frac, slot_idx -def test_stage2_autoregressive_history_attention_raises(): - with pytest.raises(NotImplementedError): - _build_stage2_ar("onehot", "wgan", history="attention") +def test_stage2_autoregressive_history_invalid_raises(): + with pytest.raises(ValueError): + _build_stage2_ar("onehot", "wgan", history="bogus") @pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) @pytest.mark.parametrize("generator", ["wgan", "flow"]) -def test_stage2_autoregressive_forward_shape(target, generator): +@pytest.mark.parametrize("history", ["markov", "attention"]) +def test_stage2_autoregressive_forward_shape(target, generator, history): B, K, emb_dim = 4, 5, 6 - model = _build_stage2_ar(target, generator, emb_dim=emb_dim, k_max=K) + model = _build_stage2_ar( + target, generator, emb_dim=emb_dim, k_max=K, history=history + ) cond_cont = torch.randn(B, COND_DIM) cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) @@ -518,3 +586,45 @@ def test_stage2_autoregressive_gradients_flow_onehot(): (flow_out + nsec_out + type_out).backward() for name, p in model.named_parameters(): assert p.grad is not None, f"no grad for {name}" + + +def test_stage2_autoregressive_history_step_matches_parallel_history_encoder(): + """`init_history_cache`/`history_step` (the incremental path + `giant/sample.py`'s AR loop drives, one slot per call) must reproduce + exactly what one parallel `self.history_encoder(history_feat, has_prev)` + call over the whole shifted sequence would give at each position — the + KV-cache correctness guarantee, exercised through `Stage2Autoregressive` + itself rather than `AttentionHistory` in isolation + (`test_attention_history_step_matches_forward` covers that lower layer).""" + B, K, emb_dim = 3, 6, 6 + model = _build_stage2_ar( + "physical", "wgan", emb_dim=emb_dim, k_max=K, history="attention" + ) + model.eval() + type_dim = stage2_type_dim({"target": "physical"}, emb_dim) + hist_in_dim = CONT_SLOT_DIM + type_dim + own_feat = torch.randn(B, K, hist_in_dim) # token i's own raw feature + has_prev_full = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) + history_feat = torch.cat( + [torch.zeros_like(own_feat[:, :1]), own_feat[:, :-1]], dim=1 + ) + + with torch.no_grad(): + expected = model.history_encoder(history_feat, has_prev_full) + + cache = model.init_history_cache() + outs = [] + prev = torch.zeros(B, 1, hist_in_dim) + for k in range(K): + has_prev_k = torch.full((B, 1), k >= 1, dtype=torch.bool) + hist_k, cache = model.history_step(prev, has_prev_k, cache) + outs.append(hist_k) + prev = own_feat[:, k : k + 1] + stepped = torch.cat(outs, dim=1) + + assert torch.allclose(stepped, expected, atol=1e-5) + + +def test_stage2_autoregressive_init_history_cache_is_none_for_markov(): + model = _build_stage2_ar("physical", "wgan", history="markov") + assert model.init_history_cache() is None diff --git a/tests/test_sample.py b/tests/test_sample.py index 670942a..cb8bdfd 100644 --- a/tests/test_sample.py +++ b/tests/test_sample.py @@ -76,6 +76,7 @@ def _stage2_ar( pdg: int = 3, mat: int = 2, k_max: int = 5, + history: str = "markov", ) -> Stage2Autoregressive: particle_cfg, material_cfg = _particle_material_cfg( _conditioning_for(target), emb_dim @@ -92,6 +93,9 @@ def _stage2_ar( noise_dim=8, k_max=k_max, particle_type_cfg={"target": target}, + history=history, + attn_n_heads=2, + attn_n_layers=1, ).eval() @@ -187,11 +191,14 @@ def test_sample_secondaries_wgan_shapes_by_target(target): # ── Stage2Autoregressive ───────────────────────────────────────────────────── +@pytest.mark.parametrize("history", ["markov", "attention"]) @pytest.mark.parametrize("generator", ["flow", "wgan"]) @pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) -def test_sample_secondaries_ar_shapes(target, generator): +def test_sample_secondaries_ar_shapes(target, generator, history): B, k_max, emb_dim = 4, 5, 6 - decoder = _stage2_ar(target, generator, emb_dim=emb_dim, k_max=k_max) + decoder = _stage2_ar( + target, generator, emb_dim=emb_dim, k_max=k_max, history=history + ) cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, k_max + 1, (B,)) diff --git a/tests/test_train.py b/tests/test_train.py index 8318add..3e37769 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -2,6 +2,7 @@ import copy import csv +import math import tempfile from pathlib import Path @@ -29,6 +30,7 @@ from giant.train import ( _relax_onehot_type_slice, _remaining_energy_fraction, _shift_prev, + _stage2_tf_prob, _stick_fraction, _type_repr, _wandb_run_config, @@ -114,6 +116,37 @@ def test_ar_has_prev_false_only_at_slot_zero(): assert has_prev.tolist() == [[False, True, True, True, True]] +# --- _stage2_tf_prob (docs/v0.3.0-design.md §3.3, v0.3.0 step 7) ----------- + + +def test_stage2_tf_prob_always_is_constant_one(): + assert _stage2_tf_prob("always", 1.0, 0.0, 0, 10) == 1.0 + assert _stage2_tf_prob("always", 1.0, 0.0, 9, 10) == 1.0 + + +def test_stage2_tf_prob_never_is_constant_zero(): + assert _stage2_tf_prob("never", 1.0, 1.0, 0, 10) == 0.0 + assert _stage2_tf_prob("never", 1.0, 1.0, 9, 10) == 0.0 + + +def test_stage2_tf_prob_scheduled_interpolates_linearly(): + assert _stage2_tf_prob("scheduled", 1.0, 0.0, 0, 11) == 1.0 + assert abs(_stage2_tf_prob("scheduled", 1.0, 0.0, 5, 11) - 0.5) < 1e-9 + assert _stage2_tf_prob("scheduled", 1.0, 0.0, 10, 11) == 0.0 + + +def test_stage2_tf_prob_scheduled_clamps_beyond_total_epochs(): + end = _stage2_tf_prob("scheduled", 1.0, 0.0, 10, 11) + beyond = _stage2_tf_prob("scheduled", 1.0, 0.0, 50, 11) + assert beyond == end + + +def test_stage2_tf_prob_scheduled_handles_single_epoch(): + # total_epochs=1 is guarded to a denominator of 1 internally (like + # _gumbel_tau's total_steps=0 guard) — epoch=0 gives zero progress. + assert _stage2_tf_prob("scheduled", 1.0, 0.0, 0, 1) == 1.0 + + @pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) def test_type_repr_shapes_and_values(target): B, K, emb_dim = 3, 4, 6 @@ -592,20 +625,70 @@ def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): # --- AR trainer wiring (v0.3.0 step 5) -------------------------------------- -def test_build_stage_trainers_rejects_scheduled_teacher_forcing(): +@pytest.mark.parametrize("teacher_forcing", ["always", "scheduled", "never"]) +@pytest.mark.parametrize("history", ["markov", "attention"]) +@pytest.mark.parametrize("stage2_generator", ["wgan", "flow"]) +def test_build_stage_trainers_ar_scheduled_and_attention_step_runs( + teacher_forcing, history, stage2_generator +): + """v0.3.0 step 7: history='attention' and teacher_forcing in + {'scheduled', 'never'} must actually train — a stage-2 AR trainer.step() + must run and produce a finite loss, for every {history} x + {teacher_forcing} x {generator} combination.""" cfg = _base_cfg() cfg["stage2_model"]["decoder"] = "autoregressive" + cfg["stage2_model"]["generator"] = stage2_generator cfg["stage2_model"]["autoregressive"] = { - "history": "markov", - "teacher_forcing": "scheduled", + "history": history, + "teacher_forcing": teacher_forcing, + "tf_p_start": 1.0, + "tf_p_end": 0.0, + "attn_n_heads": 2, + "attn_n_layers": 1, } model_config = _model_config(cfg) models = build_models(model_config) critics = build_critics(model_config) - with pytest.raises(NotImplementedError): - _build_stage_trainers( - cfg, models, critics, torch.device("cpu"), total_train_batches=4 + trainers = _build_stage_trainers( + cfg, models, critics, torch.device("cpu"), total_train_batches=4 + ) + trainer = trainers["stage2"] + batch = _fake_batches(1, 4)[0] + stats = trainer.step(batch, torch.device("cpu"), global_step=1) + loss_key = "g_loss" if stage2_generator == "wgan" else "loss" + assert math.isfinite(stats[loss_key]) + + +@pytest.mark.parametrize("stage2_generator", ["wgan", "flow"]) +def test_train_end_to_end_ar_attention_history_scheduled_teacher_forcing( + stage2_generator, +): + """Full `train()` run (not just one `trainer.step()` call) with + history='attention' AND teacher_forcing='scheduled' together — the + combination v0.3.0 step 7 exists to land — must complete and write a + checkpoint + metrics.csv with finite losses throughout.""" + cfg = _base_cfg() + cfg["stage2_model"]["decoder"] = "autoregressive" + cfg["stage2_model"]["generator"] = stage2_generator + cfg["stage2_model"]["autoregressive"] = { + "history": "attention", + "teacher_forcing": "scheduled", + "tf_p_start": 1.0, + "tf_p_end": 0.0, + "attn_n_heads": 2, + "attn_n_layers": 1, + } + with tempfile.TemporaryDirectory() as tmp: + out_dir = Path(tmp) / "run" + _run_train(cfg, out_dir) + assert (out_dir / "last.pt").exists() + with open(out_dir / "metrics.csv", newline="") as f: + rows = list(csv.DictReader(f)) + assert len(rows) == cfg["train"]["epochs"] + loss_col = ( + "stage2_train_g_loss" if stage2_generator == "wgan" else "stage2_train_loss" ) + assert all(math.isfinite(float(r[loss_col])) for r in rows) def test_ar_wgan_onehot_grad_norm_instrumentation_populates_metrics(): -- 2.39.5 From da7cde3ef9670d9fd9f25a3ec6463c0e8b0e1d45 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 7 Aug 2026 16:12:58 +0200 Subject: [PATCH 11/30] v0.3.0 post-implementation audit: resolve all 9 tracked discrepancies MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Works through docs/v0.3.0-followups.md item by item, closing the gap between the design doc and the shipped v0.3.0-stage2-autoregressive code: 1. validate.py: 7-tuple batch unpacking, sample_stage1/sample_stage2 dispatch, stage-2 particle-type-class marginal. 2. Stage-prefixed --stage1-*/--stage2-* CLI flags for train/new-run. 3. Thread stage2_model.k_max through loader/transforms/dataset/pipeline/ train instead of the hardcoded K_MAX constant. 4. Mixed conditioning.particle.type / conditioning.material.type support end-to-end (data pipeline + dwarf warm-cache). 5. conditioning.share_stages = true: one shared ConditionEncoder instance across both stages. 6. stage2_model.generator = "ddpm" formally deferred into design doc §11.2 (was silently unimplemented). 7. giant predict/rollout: implement conditioning.*.type = "onehot" via the checkpoint's saved pdg_topn_map/mat_topn_map. 8. network.py's checkpoint-path model_config migration now fails loudly on non-zero legacy expert_hidden_dim/expert_n_blocks, matching config.py's TOML-load path (§4.2). 9. validate_config now rejects stage2_model.n_sec.mode = "truth" for a rollout-capable checkpoint (§9). Also cleared all pre-existing `ty check` noise (44 -> 0 diagnostics), mostly a test-helper dict-unpack pattern that made every unrelated constructor keyword look like a type error. Co-Authored-By: Claude Sonnet 5 --- docs/v0.3.0-design.md | 7 + docs/v0.3.0-followups.md | 37 +++ giant/analysis/router_gating.py | 32 ++- giant/cli.py | 425 +++++++++++++++++++++++++----- giant/config.py | 27 +- giant/data/dataset.py | 25 +- giant/data/loader.py | 30 ++- giant/data/setup_cache.py | 18 +- giant/data/transforms.py | 177 ++++++++----- giant/model/network.py | 74 +++++- giant/model/schedule.py | 7 +- giant/pipeline.py | 57 ++-- giant/rollout.py | 58 +++- giant/train.py | 67 ++--- giant/validate.py | 251 +++++++++++------- scripts/dwarf.py | 25 +- scripts/warm_setup_cache.py | 25 +- tests/test_cli_predict.py | 59 ----- tests/test_cli_train_overrides.py | 97 +++++++ tests/test_config.py | 59 ++++- tests/test_dataset.py | 3 +- tests/test_dwarf.py | 6 +- tests/test_flow.py | 4 +- tests/test_migration_v02_v03.py | 50 ++++ tests/test_network.py | 53 ++++ tests/test_pipeline.py | 69 +++++ tests/test_rollout.py | 140 ++++++++-- tests/test_sample.py | 2 +- tests/test_transforms.py | 56 +++- tests/test_validate.py | 93 +++++-- tests/test_wgan.py | 2 +- 31 files changed, 1536 insertions(+), 499 deletions(-) create mode 100644 docs/v0.3.0-followups.md create mode 100644 tests/test_cli_train_overrides.py diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md index b5ebe64..7a47aee 100644 --- a/docs/v0.3.0-design.md +++ b/docs/v0.3.0-design.md @@ -892,6 +892,13 @@ All in `giant/config.py`: undesigned:** the mechanism should be worked out on its own terms when it is taken up, not pre-shaped by choices made for this refactor. Adding the block later is a config addition, not a break. +- **`stage2_model.generator = "ddpm"`.** The value is **accepted by the + schema** (§3.3 lists `"flow" | "ddpm" | "wgan"` with no caveat) but + `FlowDDPMStageTrainer.__init__` (`giant/train.py`) raises + `NotImplementedError` for stage 2 — only `"flow"` and `"wgan"` have a + stage-2 secondary-decoder loss implemented. `stage1_model.generator = + "ddpm"` is unaffected; this restriction is stage-2-only. Landing stage-2 + ddpm later is a trainer addition, not a config break. ### 11.3 Tracked as implementation work diff --git a/docs/v0.3.0-followups.md b/docs/v0.3.0-followups.md new file mode 100644 index 0000000..48ea25b --- /dev/null +++ b/docs/v0.3.0-followups.md @@ -0,0 +1,37 @@ +# v0.3.0 — post-implementation audit: open discrepancies + +**Status:** steps 1–7 of `docs/v0.3.0-design.md` §12 are implemented (branch +`v0.3.0-stage2-autoregressive`, commits `eb6dd27`..`200c6d2`). This document +tracked discrepancies found between that implementation and the design contract +during a 2026-08-07 audit, as concrete work items. **All items (1-9) are now +resolved** — either implemented (1-5, 7-9) or explicitly deferred into +`docs/v0.3.0-design.md` §11.2 (6: `stage2_model.generator = "ddpm"`). Step 8 +(`estimate_batch_size` recalibration) was intentionally still outstanding per +§12 and was never tracked here. + +--- + +## Confirmed correct during the audit (no action needed) + +For reference — these were explicitly checked against the design doc and +match it, including two spots the doc itself flagged as likely stale that +turned out fine: + +- Config schema, `DEFAULT_CONFIG`, `migrate_config` table (§4), `save_config`/ + `merge_cli_overrides` recursion, `default_out_dir_name`, `Conditioning` enum, + `n_sec.mode = "stop_token"` error (§9, §11.2). +- `network.py`'s full class decomposition (§5.3), dict-returning + `build_models`/`build_critics` (§5.4), `ExpertTrunk` separate in/out dims, + ST-Gumbel wiring (§2.1), AR token layout (§6.1), Markov/Attention history + encoders (§6.2). +- Shared PDG top-N type map (one map, not two, per §8), `other_policy` + sample/modal/drop (§11.1), embedding L1-nearest decode, and the L1-distance + diagnostic surfaced in `giant analyze` (§11.3). +- `analysis/render.py`/`analysis/router_gating.py` correctly branch + old-flat vs new-nested `model_config["router"]` location — doc flagged this + as a likely stale spot (§10) but it's actually fine. +- `train.py`'s per-stage trainers, mixed flow+wgan runs, WGAN critic cadence, + per-stage router auxiliary losses, stage-prefixed metrics, stage-2-only + training via ground-truth `x1_s1` (§7). +- `pipeline.py`'s deleted wgan+router rejection, per-stage `centers_init` + seeding, removed stale expert-size warning (§9, §10). diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index 9dd6a96..edfb826 100644 --- a/giant/analysis/router_gating.py +++ b/giant/analysis/router_gating.py @@ -61,21 +61,26 @@ class _RouterHandle: pdg_map: dict[int, int] mat_map: dict[str, int] cond_normalizer: "Normalizer" - conditioning: str + particle_conditioning: str + material_conditioning: str router_type: str -def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: - """The single conditioning-mode string `giant.data.transforms. - build_cond_features` still expects — from either a v0.2 checkpoint's - flat `model_config["conditioning"]` (already a string) or a new-format - one (`model_config["conditioning"]["particle"]["type"]`; `validate_config` - guarantees particle/material agree, see its mixed-conditioning check). - Mirrors `giant.cli._conditioning_str`.""" +def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str, str]: + """(particle_conditioning, material_conditioning) for + `giant.data.transforms.build_cond_features` — from either a v0.2 + checkpoint's flat `model_config["conditioning"]` (one shared string, same + for both axes) or a new-format one (independent + `model_config["conditioning"]["particle"/"material"]["type"]` — + docs/v0.3.0-design.md §3.1: the two axes may differ). Mirrors + `giant.cli._conditioning_axes`.""" raw = model_cfg.get("conditioning", default) if isinstance(raw, dict): - return raw.get("particle", {}).get("type", default) - return raw + return ( + raw.get("particle", {}).get("type", default), + raw.get("material", {}).get("type", default), + ) + return raw, raw def load_router(checkpoint: str | Path) -> _RouterHandle | None: @@ -107,12 +112,14 @@ def load_router(checkpoint: str | Path) -> _RouterHandle | None: if router is None: return None + particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) return _RouterHandle( router=router, pdg_map={int(k): v for k, v in ckpt["pdg_map"].items()}, mat_map={str(k): v for k, v in ckpt["mat_map"].items()}, cond_normalizer=Normalizer.from_dict(ckpt["normalizer"]["cond"]), - conditioning=_conditioning_str(model_cfg), + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, router_type=router_cfg["type"], ) @@ -169,7 +176,8 @@ def _gate_for_df( handle.pdg_map, handle.mat_map, cond_normalizer=handle.cond_normalizer, - conditioning=handle.conditioning, + particle_conditioning=handle.particle_conditioning, + material_conditioning=handle.material_conditioning, ) with torch.no_grad(): gate = handle.router.gate( diff --git a/giant/cli.py b/giant/cli.py index d476390..81ac407 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -19,6 +19,7 @@ from tqdm import tqdm from giant import config as gconfig from giant.constants import ( + K_MAX, LOCAL_TARGET_NAMES, PREDICT_COORD_METADATA_KEY, PREDICT_SCHEMA_VERSION, @@ -72,44 +73,21 @@ def _router_total_experts(router_cfg: dict) -> int: return int(router_cfg.get("n_experts", 1)) -def _conditioning_str(model_cfg: dict, default: str = "embedding") -> str: - """The single conditioning-mode string `giant.data.transforms. - build_cond_features` still expects — from either a v0.2 checkpoint's - flat `model_config["conditioning"]` (already a string) or a new-format - one (`model_config["conditioning"]["particle"]["type"]`; `validate_config` - guarantees particle/material agree, see its mixed-conditioning check).""" +def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str, str]: + """(particle_conditioning, material_conditioning) for + `giant.data.transforms.build_cond_features`/`build_features` — from + either a v0.2 checkpoint's flat `model_config["conditioning"]` (one + shared string, same for both axes) or a new-format one (independent + `model_config["conditioning"]["particle"/"material"]["type"]` — + docs/v0.3.0-design.md §3.1: the two axes are configured independently and + may differ).""" raw = model_cfg.get("conditioning", default) if isinstance(raw, dict): - return raw.get("particle", {}).get("type", default) - return raw - - -def _check_conditioning_onehot_support(model_cfg: dict, command: str) -> None: - """`giant predict`/`giant rollout` don't yet support - `conditioning.particle/material.type = "onehot"` — that needs - `pdg_topn_map.class_map`/`mat_topn_map.class_map` threaded into every - `build_cond_features` call in this module and `giant/rollout.py`'s - `_step_chunk`, which hasn't been wired (a separate axis from - `stage2_model.particle_type.target`, handled below — see - docs/v0.3.0-design.md §3.1 vs §3.3). Without this guard, predict/rollout - would silently build a `cond_cat` missing the topN columns - `ConditionEncoder`'s `"onehot"` mode expects. A v0.2 (flat) `model_config` - never has this, so this is a no-op there. - """ - conditioning = model_cfg.get("conditioning") - if not isinstance(conditioning, dict): - return - particle_type = conditioning.get("particle", {}).get("type") - material_type = conditioning.get("material", {}).get("type") - if particle_type == "onehot" or material_type == "onehot": - typer.echo( - f"error: giant {command} does not yet support " - "conditioning.particle/material.type = 'onehot' — only " - "stage2_model.particle_type.target in ('onehot', 'embedding') " - "is implemented (docs/v0.3.0-design.md step 6).", - err=True, + return ( + raw.get("particle", {}).get("type", default), + raw.get("material", {}).get("type", default), ) - raise typer.Exit(1) + return raw, raw def _stage_cfg(model_cfg: dict, stage: str) -> dict: @@ -144,6 +122,14 @@ def _load_pdg_topn_map(ckpt: dict): return topnmap_from_json(raw, axis="pdg") if raw is not None else None +def _load_mat_topn_map(ckpt: dict): + """`ckpt["mat_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if + this checkpoint's `conditioning.material.type` was never `"onehot"` (see + `giant.pipeline.run_setup_stage`).""" + raw = ckpt.get("mat_topn_map") + return topnmap_from_json(raw, axis="material") if raw is not None else None + + def _batch_size_estimate_dims( model_cfg: dict, training: bool, stage: str = "stage1" ) -> tuple[int, int]: @@ -295,6 +281,16 @@ class Mode(str, Enum): wgan = "wgan" +class Decoder(str, Enum): + one_shot = "one_shot" + autoregressive = "autoregressive" + + +class Stage1Context(str, Enum): + truth = "truth" + sampled = "sampled" + + # Conditioning itself lives in giant.config (imported below as gconfig) — # shared with scripts/dwarf.py's Typer commands so the two CLIs can't # silently drift apart on the option's valid values. @@ -389,6 +385,89 @@ def train( "--dropout", "-d", help="Dropout probability in ResBlocks (default: 0.1)" ), ] = None, + stage1_generator: Annotated[ + Optional[Mode], + typer.Option( + "--stage1-generator", + help="Stage 1's generative objective — overrides --mode for stage 1 " + "only (see docs/v0.3.0-design.md decision 7)", + ), + ] = None, + stage1_hidden_dim: Annotated[ + Optional[int], + typer.Option( + "--stage1-hidden-dim", + help="Overrides --hidden-dim for stage 1 only (same effect today; " + "--hidden-dim is kept as a shorthand since stage 1 was the only " + "target before stage2_model got its own flags)", + ), + ] = None, + stage1_n_res_blocks: Annotated[ + Optional[int], + typer.Option( + "--stage1-n-res-blocks", help="Overrides --n-blocks for stage 1 only" + ), + ] = None, + stage1_dropout: Annotated[ + Optional[float], + typer.Option("--stage1-dropout", help="Overrides --dropout for stage 1 only"), + ] = None, + stage2_generator: Annotated[ + Optional[Mode], + typer.Option( + "--stage2-generator", + help="Stage 2's generative objective — overrides --mode for stage 2 " + "only, e.g. combine with --stage1-generator flow for a mixed " + "flow/wgan run", + ), + ] = None, + stage2_hidden_dim: Annotated[ + Optional[int], + typer.Option("--stage2-hidden-dim", help="Stage 2 trunk width"), + ] = None, + stage2_n_res_blocks: Annotated[ + Optional[int], + typer.Option("--stage2-n-res-blocks", help="Stage 2 trunk depth"), + ] = None, + stage2_dropout: Annotated[ + Optional[float], + typer.Option("--stage2-dropout", help="Dropout inside stage 2's ResBlocks"), + ] = None, + stage2_decoder: Annotated[ + Optional[Decoder], + typer.Option( + "--stage2-decoder", + help="one_shot: predict all k_max secondary slots at once (v0.2 " + "behaviour). autoregressive: emit one secondary at a time in " + "descending-energy order (default)", + ), + ] = None, + stage2_k_max: Annotated[ + Optional[int], + typer.Option( + "--stage2-k-max", + help="Maximum secondary slots (fixed width under one_shot, a " + "generation-loop safety cap under autoregressive; default: 15)", + ), + ] = None, + stage2_context_dim: Annotated[ + Optional[int], + typer.Option( + "--stage2-context-dim", + help="Width of the projected stage-1 outcome fed into stage 2's " + "conditioning (default: 64)", + ), + ] = None, + stage2_stage1_context: Annotated[ + Optional[Stage1Context], + typer.Option( + "--stage2-stage1-context", + help="What stage 2 conditions on during training: 'truth' (the " + "ground-truth stage-1 target, detached — default) or 'sampled' " + "(stage 1's own sampled output, closing the train/inference gap " + "at the cost of an extra sampling pass per batch)", + ), + ] = None, conditioning: Annotated[ Optional[Conditioning], typer.Option( @@ -457,6 +536,52 @@ def train( "(default: same as --lr)", ), ] = None, + stage1_n_critic: Annotated[ + Optional[int], + typer.Option("--stage1-n-critic", help="Overrides --n-critic for stage 1 only"), + ] = None, + stage1_gp_weight: Annotated[ + Optional[float], + typer.Option( + "--stage1-gp-weight", help="Overrides --gp-weight for stage 1 only" + ), + ] = None, + stage1_noise_dim: Annotated[ + Optional[int], + typer.Option( + "--stage1-noise-dim", help="Overrides --noise-dim for stage 1 only" + ), + ] = None, + stage1_critic_lr: Annotated[ + Optional[float], + typer.Option( + "--stage1-critic-lr", help="Overrides --critic-lr for stage 1 only" + ), + ] = None, + stage2_n_critic: Annotated[ + Optional[int], + typer.Option("--stage2-n-critic", help="Overrides --n-critic for stage 2 only"), + ] = None, + stage2_gp_weight: Annotated[ + Optional[float], + typer.Option( + "--stage2-gp-weight", help="Overrides --gp-weight for stage 2 only" + ), + ] = None, + stage2_noise_dim: Annotated[ + Optional[int], + typer.Option( + "--stage2-noise-dim", + help="Overrides --noise-dim for stage 2 only; under " + "--stage2-decoder autoregressive a fresh draw is made per token", + ), + ] = None, + stage2_critic_lr: Annotated[ + Optional[float], + typer.Option( + "--stage2-critic-lr", help="Overrides --critic-lr for stage 2 only" + ), + ] = None, val_fraction: Annotated[ Optional[float], typer.Option("--val-fraction", "-f") ] = None, @@ -597,9 +722,11 @@ def train( }.items() if v is not None } - # Architecture-shape flags apply to stage1_model only (decision: stage 2 - # is tuned via a hand-edited config.toml until stage-prefixed --stage2-* - # flags land — see docs/v0.3.0-design.md decision 7). + # --hidden-dim/--n-blocks/--dropout are stage-1-only shorthands kept for + # backward compatibility (they predate stage2_model having its own + # flags); --stage1-*/--stage2-* below are the explicit, discoverable + # per-stage flags docs/v0.3.0-design.md decision 7 calls for, and take + # precedence when both are given. cli_stage1_model: dict[str, object] = { k: v for k, v in { @@ -609,6 +736,32 @@ def train( }.items() if v is not None } + cli_stage1_model.update( + { + k: v + for k, v in { + "hidden_dim": stage1_hidden_dim, + "n_res_blocks": stage1_n_res_blocks, + "dropout": stage1_dropout, + }.items() + if v is not None + } + ) + cli_stage2_model: dict[str, object] = { + k: v + for k, v in { + "hidden_dim": stage2_hidden_dim, + "n_res_blocks": stage2_n_res_blocks, + "dropout": stage2_dropout, + "decoder": stage2_decoder.value if stage2_decoder is not None else None, + "k_max": stage2_k_max, + "context_dim": stage2_context_dim, + "stage1_context": stage2_stage1_context.value + if stage2_stage1_context is not None + else None, + }.items() + if v is not None + } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) if cli_router: cli_stage1_model["router"] = cli_router @@ -628,37 +781,65 @@ def train( overrides["train"] = cli_train if cli_stage1_model: overrides["stage1_model"] = cli_stage1_model + if cli_stage2_model: + overrides["stage2_model"] = cli_stage2_model if cli_conditioning: overrides["conditioning"] = cli_conditioning - # --mode/--n-critic/--gp-weight/--critic-lr apply to BOTH stages — v0.2 - # had one global mode/wgan config shared by both (see - # giant.config.migrate_config's train.mode / - # train.{n_critic,gp_weight,critic_lr} precedent), unlike the - # architecture-shape flags above. + # --mode/--n-critic/--gp-weight/--critic-lr/--noise-dim apply to BOTH + # stages by default (v0.2 had one global mode/wgan config shared by both + # — see giant.config.migrate_config's train.mode / + # train.{n_critic,gp_weight,critic_lr} precedent); the --stage1-*/ + # --stage2-* variants below override a single stage independently (decision + # 7), which is what actually enables e.g. `--stage1-generator flow + # --stage2-generator wgan`. if mode is not None: overrides.setdefault("stage1_model", {})["generator"] = mode.value overrides.setdefault("stage2_model", {})["generator"] = mode.value - if noise_dim is not None: - overrides.setdefault("stage1_model", {}).setdefault("wgan", {})["noise_dim"] = ( - noise_dim - ) - wgan_overrides = { + if stage1_generator is not None: + overrides.setdefault("stage1_model", {})["generator"] = stage1_generator.value + if stage2_generator is not None: + overrides.setdefault("stage2_model", {})["generator"] = stage2_generator.value + + shared_wgan_overrides = { k: v for k, v in { "n_critic": n_critic, "gp_weight": gp_weight, + "noise_dim": noise_dim, "critic_lr": critic_lr, }.items() if v is not None } - if wgan_overrides: - overrides.setdefault("stage1_model", {}).setdefault("wgan", {}).update( - wgan_overrides - ) - overrides.setdefault("stage2_model", {}).setdefault("wgan", {}).update( - wgan_overrides - ) + stage1_wgan_overrides = { + k: v + for k, v in { + "n_critic": stage1_n_critic, + "gp_weight": stage1_gp_weight, + "noise_dim": stage1_noise_dim, + "critic_lr": stage1_critic_lr, + }.items() + if v is not None + } + stage2_wgan_overrides = { + k: v + for k, v in { + "n_critic": stage2_n_critic, + "gp_weight": stage2_gp_weight, + "noise_dim": stage2_noise_dim, + "critic_lr": stage2_critic_lr, + }.items() + if v is not None + } + for stage_name, stage_specific in ( + ("stage1_model", stage1_wgan_overrides), + ("stage2_model", stage2_wgan_overrides), + ): + stage_wgan = {**shared_wgan_overrides, **stage_specific} + if stage_wgan: + overrides.setdefault(stage_name, {}).setdefault("wgan", {}).update( + stage_wgan + ) cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) gconfig.validate_config(cfg) @@ -735,6 +916,36 @@ def new_run( n_blocks: Annotated[Optional[int], typer.Option("--n-blocks", "-n")] = None, emb_dim: Annotated[Optional[int], typer.Option("--emb-dim", "-E")] = None, dropout: Annotated[Optional[float], typer.Option("--dropout", "-d")] = None, + stage1_generator: Annotated[ + Optional[Mode], typer.Option("--stage1-generator") + ] = None, + stage1_hidden_dim: Annotated[ + Optional[int], typer.Option("--stage1-hidden-dim") + ] = None, + stage1_n_res_blocks: Annotated[ + Optional[int], typer.Option("--stage1-n-res-blocks") + ] = None, + stage1_dropout: Annotated[Optional[float], typer.Option("--stage1-dropout")] = None, + stage2_generator: Annotated[ + Optional[Mode], typer.Option("--stage2-generator") + ] = None, + stage2_hidden_dim: Annotated[ + Optional[int], typer.Option("--stage2-hidden-dim") + ] = None, + stage2_n_res_blocks: Annotated[ + Optional[int], typer.Option("--stage2-n-res-blocks") + ] = None, + stage2_dropout: Annotated[Optional[float], typer.Option("--stage2-dropout")] = None, + stage2_decoder: Annotated[ + Optional[Decoder], typer.Option("--stage2-decoder") + ] = None, + stage2_k_max: Annotated[Optional[int], typer.Option("--stage2-k-max")] = None, + stage2_context_dim: Annotated[ + Optional[int], typer.Option("--stage2-context-dim") + ] = None, + stage2_stage1_context: Annotated[ + Optional[Stage1Context], typer.Option("--stage2-stage1-context") + ] = None, conditioning: Annotated[ Optional[Conditioning], typer.Option("--conditioning") ] = None, @@ -802,6 +1013,32 @@ def new_run( }.items() if v is not None } + cli_stage1_model.update( + { + k: v + for k, v in { + "hidden_dim": stage1_hidden_dim, + "n_res_blocks": stage1_n_res_blocks, + "dropout": stage1_dropout, + }.items() + if v is not None + } + ) + cli_stage2_model: dict[str, object] = { + k: v + for k, v in { + "hidden_dim": stage2_hidden_dim, + "n_res_blocks": stage2_n_res_blocks, + "dropout": stage2_dropout, + "decoder": stage2_decoder.value if stage2_decoder is not None else None, + "k_max": stage2_k_max, + "context_dim": stage2_context_dim, + "stage1_context": stage2_stage1_context.value + if stage2_stage1_context is not None + else None, + }.items() + if v is not None + } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) if cli_router: cli_stage1_model["router"] = cli_router @@ -818,11 +1055,17 @@ def new_run( overrides["train"] = cli_train if cli_stage1_model: overrides["stage1_model"] = cli_stage1_model + if cli_stage2_model: + overrides["stage2_model"] = cli_stage2_model if cli_conditioning: overrides["conditioning"] = cli_conditioning if mode is not None: overrides.setdefault("stage1_model", {})["generator"] = mode.value overrides.setdefault("stage2_model", {})["generator"] = mode.value + if stage1_generator is not None: + overrides.setdefault("stage1_model", {})["generator"] = stage1_generator.value + if stage2_generator is not None: + overrides.setdefault("stage2_model", {})["generator"] = stage2_generator.value cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) gconfig.validate_config(cfg) @@ -988,10 +1231,11 @@ def predict( raise typer.Exit(1) model_cfg = ckpt["model_config"] - _check_conditioning_onehot_support(model_cfg, "predict") pdg_topn_map = _load_pdg_topn_map(ckpt) + mat_topn_map = _load_mat_topn_map(ckpt) other_policy = _particle_type_other_policy(model_cfg) stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") + stage2_k_max = _stage_cfg(model_cfg, "stage2").get("k_max", K_MAX) if batch_size_auto: est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( @@ -1013,7 +1257,21 @@ def predict( assert batch_size_value is not None bs = batch_size_value - conditioning = _conditioning_str(model_cfg) + particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) + if particle_conditioning == "onehot" and pdg_topn_map is None: + typer.echo( + "error: checkpoint's conditioning.particle.type='onehot' but has " + "no pdg_topn_map — retrain with the current code", + err=True, + ) + raise typer.Exit(1) + if material_conditioning == "onehot" and mat_topn_map is None: + typer.echo( + "error: checkpoint's conditioning.material.type='onehot' but has " + "no mat_topn_map — retrain with the current code", + err=True, + ) + raise typer.Exit(1) pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) @@ -1050,7 +1308,18 @@ def predict( skipped = 0 unknown_pdg_counts: Counter[int] = Counter() total_rows = sum(pq.ParquetFile(path).metadata.num_rows for path in files) - chunk_iter = iter_file_chunks if coord == Coord.local else iter_cond_chunks + + def chunk_iter(path: Path, offset: int): + if coord == Coord.local: + return iter_file_chunks(path, offset=offset, k_max=stage2_k_max) + return iter_cond_chunks(path, offset=offset) + + cond_pdg_topn = ( + pdg_topn_map.class_map if particle_conditioning == "onehot" else None + ) + cond_mat_topn = ( + mat_topn_map.class_map if material_conditioning == "onehot" else None + ) def _concat( a: dict[str, np.ndarray], b: dict[str, np.ndarray] @@ -1062,12 +1331,26 @@ def predict( if coord == Coord.local: cond_cont, cond_cat, target_raw, _, _, _, _, _, _ = build_features( - piece, pdg_map, mat_map, conditioning=conditioning + piece, + pdg_map, + mat_map, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, + pdg_topn_map=cond_pdg_topn, + mat_topn_map=cond_mat_topn, + k_max=stage2_k_max, ) cond_cont = cond_norm.transform(cond_cont) else: cond_cont, cond_cat = build_cond_features( - piece, pdg_map, mat_map, cond_norm, conditioning=conditioning + piece, + pdg_map, + mat_map, + cond_norm, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, + pdg_topn_map=cond_pdg_topn, + mat_topn_map=cond_mat_topn, ) cc = torch.from_numpy(cond_cont).float().to(_device) @@ -1409,9 +1692,23 @@ def rollout( training_cfg = gconfig.load_checkpoint_config(checkpoint) model_cfg = ckpt["model_config"] - _check_conditioning_onehot_support(model_cfg, "rollout") - conditioning = _conditioning_str(model_cfg) + particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) pdg_topn_map = _load_pdg_topn_map(ckpt) + mat_topn_map = _load_mat_topn_map(ckpt) + if particle_conditioning == "onehot" and pdg_topn_map is None: + typer.echo( + "error: checkpoint's conditioning.particle.type='onehot' but has " + "no pdg_topn_map — retrain with the current code", + err=True, + ) + raise typer.Exit(1) + if material_conditioning == "onehot" and mat_topn_map is None: + typer.echo( + "error: checkpoint's conditioning.material.type='onehot' but has " + "no mat_topn_map — retrain with the current code", + err=True, + ) + raise typer.Exit(1) other_policy = _particle_type_other_policy(model_cfg) stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") stage2_ddpm_steps = _ddpm_steps(model_cfg, "stage2") @@ -1489,8 +1786,10 @@ def rollout( max_tracks_per_event=max_tracks_per_event, escape_threshold=escape_threshold, on_chunk=_write_chunk, - conditioning=conditioning, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, pdg_topn_map=pdg_topn_map, + mat_topn_map=mat_topn_map, other_policy=other_policy, seed=seed, stage1_ddpm_steps=stage1_ddpm_steps, diff --git a/giant/config.py b/giant/config.py index 4357c70..28ed354 100644 --- a/giant/config.py +++ b/giant/config.py @@ -680,18 +680,6 @@ def validate_config(cfg: dict) -> None: single block's defaults. """ particle_type = _get_path(cfg, "conditioning.particle.type") - material_type = _get_path(cfg, "conditioning.material.type") - - if particle_type != material_type: - raise ValueError( - "conditioning.particle.type " - f"({particle_type!r}) != conditioning.material.type " - f"({material_type!r}) — the data pipeline (giant/data/transforms.py's " - "build_features/build_cond_features) doesn't support mixed " - "particle/material conditioning types yet, even though " - "ConditionEncoder itself (giant/model/network.py) is already able " - "to. Not implemented in v0.3.0 — use the same type for both axes." - ) pt_target = _get_path(cfg, "stage2_model.particle_type.target") if pt_target == "embedding" and particle_type != "embedding": @@ -733,6 +721,21 @@ def validate_config(cfg: dict) -> None: "(standalone stage-2 evaluation only, never for rollout)" ) + if ( + _get_path(cfg, "stage2_model.n_sec.mode") == "truth" + and _get_path(cfg, "stage1_model.active") + and _get_path(cfg, "stage2_model.active") + ): + raise ValueError( + "stage2_model.n_sec.mode = 'truth' is invalid for a " + "rollout-capable checkpoint (both stage1_model.active and " + "stage2_model.active = true — see docs/v0.3.0-design.md §9): " + "'truth' takes n_sec from ground truth, which giant rollout " + "doesn't have. 'truth' is for standalone stage-2 evaluation " + "only — set stage1_model.active = false for that, or use " + "'head' (default) for a rollout-capable checkpoint." + ) + if _get_path(cfg, "stage2_model.decoder") == "autoregressive": history = _get_path(cfg, "stage2_model.autoregressive.history") if history not in ("markov", "attention"): diff --git a/giant/data/dataset.py b/giant/data/dataset.py index 3736b92..6f5f216 100644 --- a/giant/data/dataset.py +++ b/giant/data/dataset.py @@ -6,6 +6,7 @@ import numpy as np import torch from torch.utils.data import IterableDataset +from giant.constants import K_MAX from giant.data.loader import event_id_offset, iter_file_chunks from giant.data.transforms import Normalizer, build_features, sorted_membership @@ -46,7 +47,7 @@ class StreamingStepsDataset(IterableDataset): (see docs/v0.3.0-design.md decision 4) target_s1: (B, 9) float32 — normalised Stage-1 primary target n_sec: (B,) int64 — true secondary count per step - sec_cont: (B, K_MAX, SEC_SLOT_DIM) float32 — [stick_logit, + sec_cont: (B, k_max, SEC_SLOT_DIM) float32 — [stick_logit, local_dir, log_mass, charge] per slot (mass/charge normalised iff `sec_phys_normalizer` was given); always computed the same way regardless of @@ -54,9 +55,13 @@ class StreamingStepsDataset(IterableDataset): downstream under target="physical" proc_idx: (B,) int64 — process-class label (ProcessRouter supervision only; zeros when `proc_map` is None) - sec_type_idx: (B, K_MAX) int64 — per-slot class index into + sec_type_idx: (B, k_max) int64 — per-slot class index into `sec_type_class_map`, for particle_type.target in ("onehot", "embedding"); zeros (unused) otherwise + + `k_max` (constructor arg, default the module constant) should match + `stage2_model.k_max` (docs/v0.3.0-design.md §8) — it sets the padded + width of `sec_cont`/`sec_type_idx` above. """ def __init__( @@ -71,11 +76,13 @@ class StreamingStepsDataset(IterableDataset): shuffle_buffer: int = 65536, shuffle: bool = True, proc_map: dict[str, int] | None = None, - conditioning: str = "embedding", + particle_conditioning: str = "embedding", + material_conditioning: str = "embedding", sec_phys_normalizer: Normalizer | None = None, pdg_topn_map: dict[int, int] | None = None, mat_topn_map: dict[str, int] | None = None, sec_type_class_map: dict | None = None, + k_max: int = K_MAX, ) -> None: self.files = list(files) self._offsets = {path: event_id_offset(i) for i, path in enumerate(self.files)} @@ -89,11 +96,13 @@ class StreamingStepsDataset(IterableDataset): self.shuffle_buffer = max(shuffle_buffer, batch_size) self.shuffle = shuffle self.proc_map = proc_map - self.conditioning = conditioning + self.particle_conditioning = particle_conditioning + self.material_conditioning = material_conditioning self.sec_phys_normalizer = sec_phys_normalizer self.pdg_topn_map = pdg_topn_map self.mat_topn_map = mat_topn_map self.sec_type_class_map = sec_type_class_map + self.k_max = k_max def __iter__(self): worker_info = torch.utils.data.get_worker_info() @@ -115,7 +124,9 @@ class StreamingStepsDataset(IterableDataset): buf_n = 0 for path in files: - for chunk in iter_file_chunks(path, offset=self._offsets[path]): + for chunk in iter_file_chunks( + path, offset=self._offsets[path], k_max=self.k_max + ): mask = sorted_membership(chunk["event_id"], self._events_arr) if not mask.any(): continue @@ -140,10 +151,12 @@ class StreamingStepsDataset(IterableDataset): sec_phys_normalizer=self.sec_phys_normalizer, proc_map=self.proc_map, require_secondaries=True, - conditioning=self.conditioning, + particle_conditioning=self.particle_conditioning, + material_conditioning=self.material_conditioning, pdg_topn_map=self.pdg_topn_map, mat_topn_map=self.mat_topn_map, sec_type_class_map=self.sec_type_class_map, + k_max=self.k_max, ) buf_cont.append(cond_cont) buf_cat.append(cond_cat) diff --git a/giant/data/loader.py b/giant/data/loader.py index 40f6a6b..e5a77bf 100644 --- a/giant/data/loader.py +++ b/giant/data/loader.py @@ -6,6 +6,8 @@ import numpy as np import pandas as pd import pyarrow.parquet as pq +from giant.constants import K_MAX + # A manifest is a plain text file listing one parquet path per line, used to # name a curated subset of files (e.g. a train/holdout pool) without copying # or symlinking the underlying parquet files. Lines are resolved relative to @@ -113,9 +115,9 @@ def _pad_dir_col(dx: pd.Series, dy: pd.Series, dz: pd.Series, K: int) -> np.ndar return out -def _df_to_dict(df: pd.DataFrame, offset: int = 0) -> dict[str, np.ndarray]: - from giant.constants import K_MAX - +def _df_to_dict( + df: pd.DataFrame, offset: int = 0, k_max: int = K_MAX +) -> dict[str, np.ndarray]: has_sec_lists = "sec_E_list" in df.columns d: dict[str, np.ndarray] = { @@ -147,17 +149,19 @@ def _df_to_dict(df: pd.DataFrame, offset: int = 0) -> dict[str, np.ndarray]: } if has_sec_lists: - d["sec_E_list"] = _pad_list_col(df["sec_E_list"], K_MAX) - d["sec_pdg_list"] = _pad_list_col_int(df["sec_pdg_list"], K_MAX) + d["sec_E_list"] = _pad_list_col(df["sec_E_list"], k_max) + d["sec_pdg_list"] = _pad_list_col_int(df["sec_pdg_list"], k_max) d["sec_dir_list"] = _pad_dir_col( - df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], K_MAX + df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], k_max ) return d -def load_steps(path: str | Path, offset: int = 0) -> dict[str, np.ndarray]: - return _df_to_dict(pd.read_parquet(path), offset=offset) +def load_steps( + path: str | Path, offset: int = 0, k_max: int = K_MAX +) -> dict[str, np.ndarray]: + return _df_to_dict(pd.read_parquet(path), offset=offset, k_max=k_max) def load_event_ids(path: str | Path, offset: int = 0) -> np.ndarray: @@ -167,12 +171,16 @@ def load_event_ids(path: str | Path, offset: int = 0) -> np.ndarray: def iter_file_chunks( - path: str | Path, offset: int = 0 + path: str | Path, offset: int = 0, k_max: int = K_MAX ) -> Iterator[dict[str, np.ndarray]]: - """Yield one parquet row-group at a time so a large file never fully loads.""" + """Yield one parquet row-group at a time so a large file never fully loads. + + `k_max` sets the padded width of the sec_*_list columns (should match + `stage2_model.k_max` — see docs/v0.3.0-design.md §8); defaults to the + module constant for callers that don't care (e.g. Stage-1-only reads).""" pf = pq.ParquetFile(path) for i in range(pf.num_row_groups): - yield _df_to_dict(pf.read_row_group(i).to_pandas(), offset=offset) + yield _df_to_dict(pf.read_row_group(i).to_pandas(), offset=offset, k_max=k_max) _COND_COLS = [ diff --git a/giant/data/setup_cache.py b/giant/data/setup_cache.py index a20d0b6..ce38def 100644 --- a/giant/data/setup_cache.py +++ b/giant/data/setup_cache.py @@ -96,10 +96,22 @@ def fingerprint_files(files: list[Path]) -> list[list]: return out -def normalizer_key(val_fraction: float, seed: int, conditioning: str) -> str: +def normalizer_key( + val_fraction: float, + seed: int, + particle_conditioning: str, + material_conditioning: str, +) -> str: # .6g avoids float-repr drift (e.g. 0.1 vs 0.10000000000000002) causing - # spurious cache misses between runs with the "same" val_fraction. - return f"valfrac={val_fraction:.6g}_seed={seed}_cond={conditioning}" + # spurious cache misses between runs with the "same" val_fraction. The two + # conditioning axes are independent (docs/v0.3.0-design.md §3.1) and both + # affect which cond_cont columns are computed for real vs. zero-filled + # (giant.data.transforms._physical_cond_columns), so both must be part of + # the key or two mixed-axis runs could collide on the same cache entry. + return ( + f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}" + f"_mcond={material_conditioning}" + ) # Top-N-map axes (docs/v0.3.0-design.md §8): "pdg" keys match pdg_map's int diff --git a/giant/data/transforms.py b/giant/data/transforms.py index 562abf0..555ed22 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -2,6 +2,8 @@ import warnings import numpy as np +from giant.constants import K_MAX + _EPS = 1e-8 # Floor added to each energy fraction before taking log-ratios so the simplex @@ -719,14 +721,23 @@ def decode_secondaries( def _physical_cond_columns( - data: dict[str, np.ndarray], conditioning: str + data: dict[str, np.ndarray], + particle_conditioning: str, + material_conditioning: str, ) -> np.ndarray: """(N, PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM) physical conditioning columns. - "embedding"/"onehot" modes zero-fill (cheap, and ConditionEncoder never - reads these columns in either mode — so an unfilled giant.materials table - can never crash an "embedding"/"onehot"-mode run). "physical" mode - computes them for real: particle columns come from + The particle and material blocks are gated independently + (docs/v0.3.0-design.md §3.1: "configured independently and may mix + freely — e.g. material `physical` with particle `embedding`"), so e.g. + `particle_conditioning="embedding"` + `material_conditioning="physical"` + zero-fills only the particle columns and computes the material ones for + real. + + "embedding"/"onehot" zero-fill their block (cheap, and ConditionEncoder + never reads these columns in either mode — so an unfilled + giant.materials table can never crash an "embedding"/"onehot"-mode run). + "physical" computes it for real: particle columns come from `data["mass"]`/`data["charge"]` when the caller already knows them directly (rollout.py, for a track descended from a model-predicted secondary — see giant/rollout.py's "no snapping" design), else derived @@ -736,36 +747,47 @@ def _physical_cond_columns( """ from giant.constants import MATERIAL_PHYS_DIM, PARTICLE_PHYS_DIM - if conditioning in ("embedding", "onehot"): - n = len(next(iter(data.values()))) - return np.zeros((n, PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM), dtype=np.float32) - if conditioning != "physical": - raise ValueError(f"unknown conditioning mode {conditioning!r}") + n = len(next(iter(data.values()))) - from giant.materials import material_properties_array - from giant.particles import particle_phys_array + if particle_conditioning == "physical": + from giant.particles import particle_phys_array - if "mass" in data and "charge" in data: - mass = np.asarray(data["mass"], dtype=np.float32) - charge = np.asarray(data["charge"], dtype=np.float32) + if "mass" in data and "charge" in data: + mass = np.asarray(data["mass"], dtype=np.float32) + charge = np.asarray(data["charge"], dtype=np.float32) + else: + mass, charge = particle_phys_array(data["pdg"]).T + particle_cols = np.column_stack([log_transform(mass), charge]) + elif particle_conditioning in ("embedding", "onehot"): + particle_cols = np.zeros((n, PARTICLE_PHYS_DIM), dtype=np.float32) else: - mass, charge = particle_phys_array(data["pdg"]).T + raise ValueError( + f"unknown conditioning.particle.type {particle_conditioning!r}" + ) - z_eff, a_eff, density, x0, lambda_int = material_properties_array( - data["material"] - ).T + if material_conditioning == "physical": + from giant.materials import material_properties_array - return np.column_stack( - [ - log_transform(mass), - charge, - z_eff, - a_eff, - log_transform(density), - log_transform(x0), - log_transform(lambda_int), - ] - ).astype(np.float32) + z_eff, a_eff, density, x0, lambda_int = material_properties_array( + data["material"] + ).T + material_cols = np.column_stack( + [ + z_eff, + a_eff, + log_transform(density), + log_transform(x0), + log_transform(lambda_int), + ] + ) + elif material_conditioning in ("embedding", "onehot"): + material_cols = np.zeros((n, MATERIAL_PHYS_DIM), dtype=np.float32) + else: + raise ValueError( + f"unknown conditioning.material.type {material_conditioning!r}" + ) + + return np.column_stack([particle_cols, material_cols]).astype(np.float32) def build_cond_features( @@ -773,19 +795,24 @@ def build_cond_features( pdg_map: dict[int, int], mat_map: dict[str, int], cond_normalizer: "Normalizer | None" = None, - conditioning: str = "embedding", + particle_conditioning: str = "embedding", + material_conditioning: str = "embedding", pdg_topn_map: dict[int, int] | None = None, mat_topn_map: dict[str, int] | None = None, ) -> tuple[np.ndarray, np.ndarray]: """Build conditioning arrays only — no target, no post-step variables. + `particle_conditioning`/`material_conditioning` are independent + (docs/v0.3.0-design.md §3.1) — e.g. `particle_conditioning="embedding"` + + `material_conditioning="physical"` is a valid mix. + `pdg_topn_map`/`mat_topn_map` (a top-N-plus-other `class_map`, see `giant.data.loader.build_topn_map_from_files`) append extra `cond_cat` columns read by `ConditionEncoder`'s `"onehot"` mode (docs/v0.3.0-design.md decision 4): pdg topN index at column 2 (iff `pdg_topn_map` given), material topN index at column 3 (iff `mat_topn_map` given, after column 2 if both are). Only ever given when - `conditioning == "onehot"`; `cond_cat` stays `(N, 2)` otherwise. + the corresponding axis is `"onehot"`; `cond_cat` stays `(N, 2)` otherwise. """ cond_cont = np.column_stack( [ @@ -796,7 +823,10 @@ def build_cond_features( ] ).astype(np.float32) cond_cont = np.column_stack( - [cond_cont, _physical_cond_columns(data, conditioning)] + [ + cond_cont, + _physical_cond_columns(data, particle_conditioning, material_conditioning), + ] ).astype(np.float32) # In "physical" mode cond_cat's first two columns are only a @@ -807,10 +837,11 @@ def build_cond_features( # conditioning signal, so an unmapped value must still raise loudly # rather than silently misassign. In "onehot" mode they again go unread # (the topN columns below are the real signal), so they're as permissive - # as "physical". - strict = conditioning == "embedding" - pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map, strict=strict) - mat_idx = _vectorized_map_lookup(data["material"], mat_map, strict=strict) + # as "physical". Each axis's strictness is independent. + pdg_strict = particle_conditioning == "embedding" + mat_strict = material_conditioning == "embedding" + pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map, strict=pdg_strict) + mat_idx = _vectorized_map_lookup(data["material"], mat_map, strict=mat_strict) cat_cols = [pdg_idx, mat_idx] if pdg_topn_map is not None: cat_cols.append(_vectorized_map_lookup(data["pdg"], pdg_topn_map)) @@ -819,34 +850,44 @@ def build_cond_features( cond_cat = np.column_stack(cat_cols) if cond_normalizer is not None: - cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning) + cond_cont = _cond_normalizer_transform( + cond_cont, cond_normalizer, particle_conditioning, material_conditioning + ) return cond_cont, cond_cat def _cond_normalizer_transform( - cond_cont: np.ndarray, cond_normalizer: "Normalizer", conditioning: str + cond_cont: np.ndarray, + cond_normalizer: "Normalizer", + particle_conditioning: str, + material_conditioning: str, ) -> np.ndarray: """Apply ``cond_normalizer``, padding a legacy narrower normalizer if needed. Checkpoints trained before physical-property conditioning (``COND_DIM`` 8->15, ``giant/constants.py``) saved a ``COND_DIM_BASE``-wide (8) cond normalizer, fit before ``build_cond_features`` grew the extra physical - columns. In "embedding" mode those columns are never read by + columns. When NEITHER axis is "physical" those columns are never read by ``ConditionEncoder`` (``giant/model/network.py``), so padding the missing entries with mean=0/std=1 is a safe no-op that keeps such checkpoints - usable under the current, always-``COND_DIM``-wide contract. In - "physical" mode the physical columns are load-bearing, so a mismatch - there is a real incompatibility, not something to paper over. + usable under the current, always-``COND_DIM``-wide contract. If EITHER + axis is "physical" its columns are load-bearing, so a mismatch there is a + real incompatibility, not something to paper over. """ mean, std = cond_normalizer.mean, cond_normalizer.std assert mean is not None and std is not None, "Normalizer not fitted" width = cond_cont.shape[-1] if mean.shape[-1] < width: - if conditioning != "embedding": + physical_load_bearing = "physical" in ( + particle_conditioning, + material_conditioning, + ) + if physical_load_bearing: raise ValueError( f"cond normalizer has {mean.shape[-1]} columns, expected " - f"{width}, and conditioning={conditioning!r} reads the " + f"{width}, and particle_conditioning={particle_conditioning!r}/" + f"material_conditioning={material_conditioning!r} reads the " "physical columns directly — this checkpoint predates " "physical-property conditioning and can't be safely padded; " "retrain it under the current code." @@ -867,11 +908,13 @@ def build_features( fit: bool = False, proc_map: dict[str, int] | None = None, require_secondaries: bool = False, - conditioning: str = "embedding", + particle_conditioning: str = "embedding", + material_conditioning: str = "embedding", sec_phys_only: bool = False, pdg_topn_map: dict[int, int] | None = None, mat_topn_map: dict[str, int] | None = None, sec_type_class_map: dict | None = None, + k_max: int = K_MAX, ) -> tuple[ np.ndarray, np.ndarray, @@ -922,8 +965,14 @@ def build_features( top-N-plus-other map's `class_map` for `target = "onehot"`, or the dense `pdg_map` for `target = "embedding"` (pass `pdg_map` itself). `None` for `target = "physical"`. + + k_max: should match `stage2_model.k_max` (docs/v0.3.0-design.md §8) — + overridden internally by `data["sec_E_list"]`'s own padded width when + present (the loader already padded it to some k_max; that width is + authoritative), so this only actually matters when secondary list + columns are absent (Stage-1-only reads, or a pre-secondary-join + file), where it sets `sec_cont`/`sec_type_idx`'s zero-filled width. """ - from giant.constants import K_MAX post_dir_local = local_frame_rotation(data["pre_dir"], data["post_dir"]) travel_dir_local = local_frame_rotation( @@ -953,7 +1002,10 @@ def build_features( ] ).astype(np.float32) # (N, COND_DIM_BASE=8) cond_cont = np.column_stack( - [cond_cont, _physical_cond_columns(data, conditioning)] + [ + cond_cont, + _physical_cond_columns(data, particle_conditioning, material_conditioning), + ] ).astype(np.float32) # (N, COND_DIM=15) pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map) @@ -968,20 +1020,27 @@ def build_features( n_sec_raw = data["n_sec"].astype( np.int64 ) # (N,) unclamped, for the valid-slot mask - # Clamp the classification label to K_MAX: the head only has K_MAX+1 classes - # (0..K_MAX), and truncating here mirrors the K_MAX-slot truncation already - # applied to sec_cont by the loader's list padding. Without this, a rare - # high-multiplicity step (real data goes up to ~37) hands cross_entropy - # an out-of-range target and CUDA asserts. - n_sec = np.minimum(n_sec_raw, K_MAX).astype(np.int64) # (N,) # Secondary continuous targets sec_E_list = data.get("sec_E_list") sec_dir_list = data.get("sec_dir_list") sec_pdg_list = data.get("sec_pdg_list") + if sec_E_list is not None: + # The loader already padded sec_*_list to some k_max (see + # giant.data.loader.iter_file_chunks); that padded width is + # authoritative over whatever this call happened to pass in, so the + # two can never drift apart (docs/v0.3.0-design.md §8). + k_max = sec_E_list.shape[1] + + # Clamp the classification label to k_max: the head only has k_max+1 + # classes (0..k_max), and truncating here mirrors the k_max-slot + # truncation already applied to sec_cont by the loader's list padding. + # Without this, a rare high-multiplicity step (real data goes up to ~37) + # hands cross_entropy an out-of-range target and CUDA asserts. + n_sec = np.minimum(n_sec_raw, k_max).astype(np.int64) # (N,) if sec_E_list is not None and sec_dir_list is not None and sec_pdg_list is not None: - sec_valid = np.arange(K_MAX)[None, :] < n_sec_raw[:, None] # (N, K_MAX) + sec_valid = np.arange(k_max)[None, :] < n_sec_raw[:, None] # (N, k_max) sec_cont = encode_secondaries( sec_E_list, sec_dir_list, @@ -990,11 +1049,11 @@ def build_features( data["pre_dir"], sec_pdg_list=sec_pdg_list, phys_only=sec_phys_only, - ) # (N, K_MAX, 6) + ) # (N, k_max, 6) sec_type_idx = ( encode_secondary_type_idx(sec_pdg_list, sec_valid, sec_type_class_map) if sec_type_class_map is not None - else np.zeros((len(n_sec), K_MAX), dtype=np.int64) + else np.zeros((len(n_sec), k_max), dtype=np.int64) ) else: # Guard against silently training Stage 2 on zeroed targets: if any step @@ -1017,8 +1076,8 @@ def build_features( "require_secondaries=False for Stage-1-only use." ) N = len(n_sec) - sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32) - sec_type_idx = np.zeros((N, K_MAX), dtype=np.int64) + sec_cont = np.zeros((N, k_max, 6), dtype=np.float32) + sec_type_idx = np.zeros((N, k_max), dtype=np.int64) if fit: cond_normalizer = Normalizer().fit(cond_cont) diff --git a/giant/model/network.py b/giant/model/network.py index a058af6..f7161aa 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -990,7 +990,12 @@ class Stage1Model(nn.Module): (docs/v0.3.0-design.md §2) moves it to stage 2, except for a migrated v0.2 checkpoint (`n_sec_head_k_max` given), where it stays attached here since that's where its weights live and what conditioning it was trained - against (see `_migrate_legacy_model_config`).""" + against (see `_migrate_legacy_model_config`). + + `cond_enc`, if given, is used in place of building a fresh + `ConditionEncoder` — `conditioning.share_stages = true` (§3.1): `build_models` + constructs one shared instance and passes it to both stages, halving the + conditioning parameter count and forcing a common representation.""" def __init__( self, @@ -1008,12 +1013,17 @@ class Stage1Model(nn.Module): noise_dim: int = 64, router: Router | None = None, n_sec_head_k_max: int | None = None, + cond_enc: ConditionEncoder | None = None, ) -> None: super().__init__() self.generator_kind = generator self.noise_dim = noise_dim - self.cond_enc = ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + self.cond_enc = ( + cond_enc + if cond_enc is not None + else ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + ) ) has_time = generator in ("flow", "ddpm") self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None @@ -1083,6 +1093,9 @@ class Stage2OneShot(nn.Module): Under `generator == "wgan"` the type slice stays folded into `sec_dim` (just `emb_dim` instead of `PARTICLE_PHYS_DIM` wide) and `type_head` is unused (`None`) — the WGAN trainer handles the ST-Gumbel relaxation. + + `cond_enc`, if given, is used in place of building a fresh + `ConditionEncoder` — see `Stage1Model`'s docstring (`conditioning.share_stages`). """ def __init__( @@ -1105,6 +1118,7 @@ class Stage2OneShot(nn.Module): router: Router | None = None, build_n_sec_head: bool = True, particle_type_cfg: dict | None = None, + cond_enc: ConditionEncoder | None = None, ) -> None: super().__init__() self.generator_kind = generator @@ -1112,8 +1126,12 @@ class Stage2OneShot(nn.Module): self.k_max = k_max self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) self.type_dim = stage2_type_dim(self.particle_type_cfg, particle_cfg["emb_dim"]) - self.cond_enc = ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + self.cond_enc = ( + cond_enc + if cond_enc is not None + else ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + ) ) self.context_adapter = ContextAdapter(x_dim, context_dim) self.fuse = nn.Sequential( @@ -1227,6 +1245,9 @@ class Stage2Autoregressive(nn.Module): on token position; `_token_cond` additionally fuses in the history encoding and two running scalars (remaining energy-budget fraction, normalized slot index), and feeds `forward`/`predict_type`/the trunk. + + `cond_enc`, if given, is used in place of building a fresh + `ConditionEncoder` — see `Stage1Model`'s docstring (`conditioning.share_stages`). """ def __init__( @@ -1251,6 +1272,7 @@ class Stage2Autoregressive(nn.Module): history: str = "markov", attn_n_heads: int = 4, attn_n_layers: int = 2, + cond_enc: ConditionEncoder | None = None, ) -> None: super().__init__() if history not in ("markov", "attention"): @@ -1266,8 +1288,12 @@ class Stage2Autoregressive(nn.Module): emb_dim = particle_cfg["emb_dim"] self.type_dim = stage2_type_dim(self.particle_type_cfg, emb_dim) - self.cond_enc = ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + self.cond_enc = ( + cond_enc + if cond_enc is not None + else ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + ) ) self.context_adapter = ContextAdapter(x_dim, context_dim) self.base_fuse = nn.Sequential( @@ -1562,6 +1588,20 @@ def _migrate_legacy_model_config(model_config: dict) -> dict: k_max = m.get("k_max", K_MAX) noise_dim = m.get("noise_dim", 64) router_cfg = dict(m.get("router") or {}) + expert_hidden_dim = router_cfg.pop("expert_hidden_dim", 0) + expert_n_blocks = router_cfg.pop("expert_n_blocks", 0) + if expert_hidden_dim or expert_n_blocks: + raise ValueError( + "this checkpoint's model_config.router sets expert_hidden_dim/" + f"expert_n_blocks to a non-default value " + f"({expert_hidden_dim!r}, {expert_n_blocks!r}); v0.3.0 removed " + "per-expert sizing (experts always inherit the stage's " + "hidden_dim/n_res_blocks), so this checkpoint's routed experts " + "have a different width/depth than the monolith — silently " + "dropping these keys would resize the experts instead of " + "refusing (docs/v0.3.0-design.md §4.2). This checkpoint can " + "only be loaded by v0.2 code." + ) router_cfg.setdefault("enabled", False) return { @@ -1657,6 +1697,13 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: instance rather than building a second, independently-parameterized one (v0.2's actual — probably accidental — behaviour: two routers built from one config with no semantic relationship between them). + + `conditioning.share_stages = true` (§3.1) builds one `ConditionEncoder` + instance here and passes it to both stages (`Stage1Model`/`Stage2OneShot`/ + `Stage2Autoregressive`'s `cond_enc` param), instead of each stage + building its own — halving the conditioning parameter count and forcing a + common representation. `false` (default) keeps v0.2 behaviour: + independent instances with identical config but independent weights. """ cfg = ( model_config @@ -1666,17 +1713,17 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: pdg_vocab = cfg["pdg_vocab"] mat_vocab = cfg["mat_vocab"] conditioning = cfg["conditioning"] - if conditioning.get("share_stages"): - raise NotImplementedError( - "conditioning.share_stages = true is not implemented yet — each " - "stage always builds its own ConditionEncoder for now" - ) particle_cfg = conditioning["particle"] material_cfg = conditioning["material"] particle_conditioning = particle_cfg["type"] s1cfg = cfg["stage1_model"] s2cfg = cfg["stage2_model"] cond_out_dim = conditioning.get("out_dim", 128) + shared_cond_enc: ConditionEncoder | None = None + if conditioning.get("share_stages"): + shared_cond_enc = ConditionEncoder( + pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim + ) result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} @@ -1707,6 +1754,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: noise_dim=(s1cfg.get("wgan") or {}).get("noise_dim", 64), router=stage1_router, n_sec_head_k_max=n_sec_head_k_max, + cond_enc=shared_cond_enc, ) if s2cfg.get("active", True): @@ -1748,6 +1796,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: history=ar_cfg.get("history", "markov"), attn_n_heads=ar_cfg.get("attn_n_heads", 4), attn_n_layers=ar_cfg.get("attn_n_layers", 2), + cond_enc=shared_cond_enc, ) else: sec_dim = stage2_trunk_sec_dim( @@ -1771,6 +1820,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: router=stage2_router, build_n_sec_head=legacy_owner != "stage1", particle_type_cfg=particle_type_cfg, + cond_enc=shared_cond_enc, ) return result diff --git a/giant/model/schedule.py b/giant/model/schedule.py index 5c1e019..25aff5e 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -108,12 +108,13 @@ def flow_matching_loss_secondary( averaged over their own width first and then combined with equal weight — this stays correct if type_dim/CONT_SLOT_DIM change. """ - from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM + from giant.constants import CONT_SLOT_DIM, PARTICLE_PHYS_DIM if type_dim is None: type_dim = PARTICLE_PHYS_DIM B = x1.size(0) + k_max = sec_mask.size(1) t = torch.rand(B, device=x1.device) x0 = torch.randn_like(x1) x_t = (1.0 - t.view(-1, 1)) * x0 + t.view(-1, 1) * x1 @@ -121,8 +122,8 @@ def flow_matching_loss_secondary( v_t = model(x_t, cond_cont, cond_cat, stage1_out, t=t) slot_dim = CONT_SLOT_DIM + type_dim - err = ((v_t - u_t) ** 2).view(B, K_MAX, slot_dim) - cont_err = err[..., :CONT_SLOT_DIM].mean(dim=-1) # (B, K_MAX) + err = ((v_t - u_t) ** 2).view(B, k_max, slot_dim) + cont_err = err[..., :CONT_SLOT_DIM].mean(dim=-1) # (B, k_max) mask = sec_mask.float() denom = mask.sum().clamp(min=1) diff --git a/giant/pipeline.py b/giant/pipeline.py index 5584996..a10eea8 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -9,7 +9,6 @@ from torch.utils.data import DataLoader from giant import config from giant.constants import ( COND_DIM, - K_MAX, PARTICLE_PHYS_DIM, X_DIM, ) @@ -106,11 +105,9 @@ def run_setup_stage( Stage-2 normalizers. `cfg` is the full merged v0.3 config (`conditioning`/`stage1_model`/ - `stage2_model`), already passed through `giant.config.validate_config` — - in particular, `conditioning.particle.type == conditioning.material.type` - is assumed (the data pipeline doesn't support mixed conditioning types - yet, see `validate_config`), so a single shared conditioning string - drives `giant.data.transforms.build_features`. + `stage2_model`), already passed through `giant.config.validate_config`. + `conditioning.particle.type` and `conditioning.material.type` are + independent (docs/v0.3.0-design.md §3.1) and may differ. Reads from and writes to the `giant.data.setup_cache` sidecar when `cache_setup` is set (`rebuild_setup_cache` ignores — but still @@ -118,7 +115,9 @@ def run_setup_stage( is mutated in place: an active `EnergyRouter` (`router.type == "energy"`) gets its `centers_init` seeded from real data quantiles here. """ - conditioning = cfg["conditioning"]["particle"]["type"] + particle_conditioning = cfg["conditioning"]["particle"]["type"] + material_conditioning = cfg["conditioning"]["material"]["type"] + k_max = cfg["stage2_model"]["k_max"] stage1_router = cfg["stage1_model"].get("router") or {} stage2_router = cfg["stage2_model"].get("router") or {} @@ -255,7 +254,9 @@ def run_setup_stage( for r in (stage1_router, stage2_router) ) energy_idx = 3 - norm_key = setup_cache.normalizer_key(val_fraction, seed, conditioning) + norm_key = setup_cache.normalizer_key( + val_fraction, seed, particle_conditioning, material_conditioning + ) entry = cache.normalizers.get(norm_key) if cache is not None else None if entry is not None: @@ -284,7 +285,7 @@ def run_setup_stage( _ReservoirSampler(capacity=100_000) if collect_energy_sample else None ) for i, path in enumerate(files): - for chunk in iter_file_chunks(path, offset=event_id_offset(i)): + for chunk in iter_file_chunks(path, offset=event_id_offset(i), k_max=k_max): mask = sorted_membership(chunk["event_id"], events_arr) if not mask.any(): continue @@ -296,15 +297,17 @@ def run_setup_stage( mat_map, proc_map=proc_map, require_secondaries=True, - conditioning=conditioning, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, sec_phys_only=True, + k_max=k_max, ) ) cond_acc.update(cond_cont) tgt_acc.update(target_s1) if energy_sampler is not None: energy_sampler.update(cond_cont[:, energy_idx]) - sec_valid = np.arange(K_MAX)[None, :] < n_sec[:, None] + sec_valid = np.arange(sec_cont.shape[1])[None, :] < n_sec[:, None] sec_phys = sec_cont[:, :, 4:6][sec_valid] if len(sec_phys) > 0: sec_phys_acc.update(sec_phys) @@ -375,7 +378,9 @@ def run_train_job( ) config.validate_config(cfg) - conditioning = cfg["conditioning"]["particle"]["type"] + particle_conditioning = cfg["conditioning"]["particle"]["type"] + material_conditioning = cfg["conditioning"]["material"]["type"] + k_max = cfg["stage2_model"]["k_max"] setup = run_setup_stage( data, val_fraction=t["val_fraction"], @@ -399,18 +404,20 @@ def run_train_job( ) # cond_cat's onehot columns (docs/v0.3.0-design.md decision 4) are - # present only under conditioning="onehot" — validate_config enforces - # conditioning.particle.type == conditioning.material.type, so both - # axes' topN columns are always present or absent together. run_setup_stage - # builds both maps whenever conditioning=="onehot" (see its own - # particle_cfg["type"] == "onehot" check), so they're guaranteed non-None - # here — asserted, not just assumed, so a future wiring bug fails loudly - # instead of silently dropping the onehot columns. + # present per-axis, independently, under that axis's own + # conditioning.{particle,material}.type == "onehot" (§3.1: the two axes + # may mix freely). run_setup_stage builds each map whenever its own axis + # is "onehot" (see its own particle_cfg["type"]/material_cfg["type"] + # checks), so they're guaranteed non-None here — asserted, not just + # assumed, so a future wiring bug fails loudly instead of silently + # dropping the onehot columns. cond_pdg_topn = None cond_mat_topn = None - if conditioning == "onehot": - assert setup.pdg_topn_map is not None and setup.mat_topn_map is not None + if particle_conditioning == "onehot": + assert setup.pdg_topn_map is not None cond_pdg_topn = setup.pdg_topn_map.class_map + if material_conditioning == "onehot": + assert setup.mat_topn_map is not None cond_mat_topn = setup.mat_topn_map.class_map # The secondary type-index map depends on stage2_model.particle_type.target, @@ -441,11 +448,13 @@ def run_train_job( shuffle_buffer=shuffle_buffer, shuffle=True, proc_map=proc_map, - conditioning=conditioning, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, sec_phys_normalizer=sec_phys_norm, pdg_topn_map=cond_pdg_topn, mat_topn_map=cond_mat_topn, sec_type_class_map=sec_type_class_map, + k_max=k_max, ) val_ds = StreamingStepsDataset( files=files, @@ -457,11 +466,13 @@ def run_train_job( batch_size=t["batch_size"], shuffle=False, proc_map=proc_map, - conditioning=conditioning, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, sec_phys_normalizer=sec_phys_norm, pdg_topn_map=cond_pdg_topn, mat_topn_map=cond_mat_topn, sec_type_class_map=sec_type_class_map, + k_max=k_max, ) pin = device.type == "cuda" diff --git a/giant/rollout.py b/giant/rollout.py index 3bca136..c38edd4 100644 --- a/giant/rollout.py +++ b/giant/rollout.py @@ -352,7 +352,7 @@ def make_seed_frontier( pre_pos: np.ndarray, pre_E: np.ndarray, pre_dir: np.ndarray, - conditioning: str = "embedding", + particle_conditioning: str = "embedding", ) -> tuple[dict[str, np.ndarray], dict[int, int]]: """Build the initial frontier from primary entry states. @@ -372,17 +372,17 @@ def make_seed_frontier( dir_ = dir_ / np.clip(np.linalg.norm(dir_, axis=1, keepdims=True), 1e-12, None) pdg_arr = np.asarray(pdg, dtype=np.int64) - if conditioning == "physical": + if particle_conditioning == "physical": # Real primaries always have a genuine ground-truth PDG code, looked # up once here and carried forward unchanged for the track's lifetime # (its species never changes mid-track) — same lifecycle as "pdg" # itself. mass, charge = particle_phys_array(pdg_arr).T else: - # "embedding" mode never reads mass/charge (see + # "embedding"/"onehot" never read mass/charge (see # _physical_cond_columns), so resolving them here would only risk - # crashing an embedding-mode rollout on a PDG code giant.particles - # can't resolve, for a value that's never used. + # crashing a rollout on a PDG code giant.particles can't resolve, for + # a value that's never used. mass = np.zeros(n, dtype=np.float64) charge = np.zeros(n, dtype=np.float64) @@ -457,8 +457,10 @@ def rollout( max_tracks_per_event: int | None = None, escape_threshold: float | None = None, on_chunk: Callable[[dict[str, np.ndarray]], None] | None = None, - conditioning: str = "embedding", + particle_conditioning: str = "embedding", + material_conditioning: str = "embedding", pdg_topn_map: "TopNMap | None" = None, + mat_topn_map: "TopNMap | None" = None, other_policy: str = "sample", seed: int | None = None, stage1_ddpm_steps: int = 1000, @@ -485,8 +487,13 @@ def rollout( autoregressive) is inferred from `sec_decoder`'s own class — see `sample_stage1`/`sample_stage2` (giant.sample). - `pdg_topn_map`/`other_policy` are only read under - `stage2_model.particle_type.target = "onehot"` (§3.3); `seed` seeds the + `pdg_topn_map`/`mat_topn_map` serve two independent purposes that happen + to share `pdg_topn_map` (docs/v0.3.0-design.md §8 — one PDG map, not + two): they're required whenever `particle_conditioning`/ + `material_conditioning` is `"onehot"` (feeds `build_cond_features`'s + extra `cond_cat` top-N columns — §3.1), and `pdg_topn_map`/`other_policy` + are additionally read under `stage2_model.particle_type.target = + "onehot"` (§3.3, secondary-species decode). `seed` seeds the `other_policy = "sample"` draw only (torch/numpy sampling itself is seeded by the caller, same as today). @@ -494,6 +501,16 @@ def rollout( diagnostic across the whole run — see `L1DistCollector`. Only populated under `particle_type.target = "embedding"`; a no-op otherwise. """ + if particle_conditioning == "onehot" and pdg_topn_map is None: + raise RuntimeError( + "conditioning.particle.type='onehot' rollout needs pdg_topn_map " + "(the checkpoint's saved top-N map) — see ckpt['pdg_topn_map']" + ) + if material_conditioning == "onehot" and mat_topn_map is None: + raise RuntimeError( + "conditioning.material.type='onehot' rollout needs mat_topn_map " + "(the checkpoint's saved top-N map) — see ckpt['mat_topn_map']" + ) device = device or torch.device("cpu") stage1_model.eval() sec_decoder.eval() @@ -507,7 +524,7 @@ def rollout( seeds["pre_pos"], seeds["pre_E"], seeds["pre_dir"], - conditioning=conditioning, + particle_conditioning=particle_conditioning, ) rec = _Recorder(sink=on_chunk) @@ -534,8 +551,10 @@ def rollout( steps, device, max_tracks_per_event, - conditioning, + particle_conditioning, + material_conditioning, pdg_topn_map, + mat_topn_map, other_policy, rng, stage1_ddpm_steps, @@ -570,8 +589,10 @@ def _step_chunk( steps, device, max_tracks_per_event, - conditioning, + particle_conditioning, + material_conditioning, pdg_topn_map, + mat_topn_map, other_policy, rng, stage1_ddpm_steps, @@ -587,7 +608,7 @@ def _step_chunk( tr["_material"] = material tr["_layer_id"] = layer_id - if conditioning == "physical": + if particle_conditioning == "physical": # Under physical-property conditioning, mass/charge (already resolved # on every track — see the cond_dict comment below) drive the model, # not a training-vocab PDG embedding — build_cond_features passes @@ -655,7 +676,18 @@ def _step_chunk( "charge": tr["charge"], } cond_cont, cond_cat = build_cond_features( - cond_dict, pdg_map, mat_map, cond_norm, conditioning=conditioning + cond_dict, + pdg_map, + mat_map, + cond_norm, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, + pdg_topn_map=pdg_topn_map.class_map + if particle_conditioning == "onehot" + else None, + mat_topn_map=mat_topn_map.class_map + if material_conditioning == "onehot" + else None, ) cc = torch.from_numpy(cond_cont).float().to(device) ck = torch.from_numpy(cond_cat).long().to(device) diff --git a/giant/train.py b/giant/train.py index 6a3689c..774638c 100644 --- a/giant/train.py +++ b/giant/train.py @@ -4,7 +4,6 @@ import math import os import signal import time -import warnings from pathlib import Path from types import FrameType from typing import Callable @@ -16,7 +15,7 @@ import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm -from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM +from giant.constants import CONT_SLOT_DIM, PARTICLE_PHYS_DIM from giant.data.loader import TopNMap from giant.data.setup_cache import topnmap_to_json from giant.model.network import Router, stage2_type_dim @@ -483,9 +482,10 @@ class FlowDDPMStageTrainer(StageTrainer): ) -> None: if is_stage2 and generator not in ("flow",): raise NotImplementedError( - f"stage2_model.generator={generator!r} is not implemented for " - "stage 2 (only 'flow' and 'wgan' — 'ddpm' has no stage-2 " - "secondary-decoder loss yet, see docs/v0.3.0-design.md)" + f"stage2_model.generator={generator!r} is accepted by the " + "schema but not implemented in v0.3.0 for stage 2 (only " + "'flow' and 'wgan' have a stage-2 secondary-decoder loss — " + "see docs/v0.3.0-design.md §11.2)" ) self.name = name self.is_stage2 = is_stage2 @@ -653,7 +653,9 @@ class FlowDDPMStageTrainer(StageTrainer): proc_idx, sec_type_idx, ) = _batch_to_device(batch, device) - sec_mask = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec.unsqueeze(1) + sec_mask = torch.arange(sec_cont.size(1), device=device).unsqueeze( + 0 + ) < n_sec.unsqueeze(1) stage1_ctx = x1_s1.detach() x1_s2 = None @@ -957,8 +959,9 @@ class WGANStageTrainer(StageTrainer): self.particle_type_cfg, self.particle_type_emb_dim ) slot_width = CONT_SLOT_DIM + type_dim + k_max = sec_cont.size(1) - sec_mask = torch.arange(K_MAX, device=device).unsqueeze( + sec_mask = torch.arange(k_max, device=device).unsqueeze( 0 ) < n_sec.unsqueeze(1) mask = ( @@ -1002,7 +1005,7 @@ class WGANStageTrainer(StageTrainer): ).reshape(B, -1) * mask ) - z = torch.randn(B, K_MAX, self.model.noise_dim, device=device) + z = torch.randn(B, k_max, self.model.noise_dim, device=device) fake_raw = self.model( z, cond_cont, @@ -1042,7 +1045,7 @@ class WGANStageTrainer(StageTrainer): self.type_gumbel_tau_end, ) fake_raw = _relax_onehot_type_slice( - fake_raw, K_MAX, CONT_SLOT_DIM, type_dim, tau, grad_probe=grad_probe + fake_raw, k_max, CONT_SLOT_DIM, type_dim, tau, grad_probe=grad_probe ) fake = fake_raw * mask @@ -1161,39 +1164,14 @@ class WGANStageTrainer(StageTrainer): group["lr"] = resumed_critic_lr -_WARNED_MARGINAL_VALIDATION_BROKEN = False - - def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): - """`giant.sample` itself has a per-stage generator/n_sec-ownership - update (v0.3.0 step 6 — `sample_stage1`/`sample_stage2`/`resolve_n_sec`, - docs/v0.3.0-design.md §10); `giant/validate.py` hasn't been updated to - call it yet, and also still unpacks the val batch as a 6-tuple, which no - longer matches `StreamingStepsDataset`'s 7-tuple shape (the `sec_type_idx` - column step 4 added). Both are `giant/validate.py`-side gaps, not - `giant/sample.py`'s — tracked, not yet scheduled. Until fixed, this - degrades gracefully with a one-time warning instead of crashing the - whole training run. - """ - global _WARNED_MARGINAL_VALIDATION_BROKEN + """Runs `validate_marginals` on `trainer`'s sampling model (EMA model if + present, else the raw model). `validate_marginals` itself dispatches + through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec` + (docs/v0.3.0-design.md §10), so this is generator- and + one-shot-vs-autoregressive-agnostic.""" model = trainer.sampling_model() - try: - return validate_marginals(model, val_loader, device=device, **kwargs) - except Exception as exc: # noqa: BLE001 — see docstring: any failure here - # is expected until giant/validate.py is updated for the v0.3.0 - # per-stage dispatch + 7-tuple batch shape. - if not _WARNED_MARGINAL_VALIDATION_BROKEN: - warnings.warn( - "marginal validation unavailable this run " - f"({type(exc).__name__}: {exc}) — giant/validate.py hasn't " - "been updated for v0.3.0's per-stage sample dispatch / " - "7-tuple val batch shape yet; val_marginal_kl stays NaN, and " - "wgan best-checkpoint selection falls back to the " - "Wasserstein-distance magnitude.", - stacklevel=2, - ) - _WARNED_MARGINAL_VALIDATION_BROKEN = True - return None + return validate_marginals(model, val_loader, device=device, **kwargs) def _build_stage_trainers( @@ -1688,7 +1666,6 @@ def train( stage1_tr, val_loader, device, - mode=cfg["stage1_model"]["generator"], sec_decoder=trainers["stage2"].sampling_model() if "stage2" in trainers else None, @@ -1716,11 +1693,11 @@ def train( stage1_tr, val_loader, device, - mode=cfg["stage1_model"]["generator"], - schedule=stage1_tr.ddpm_schedule - if isinstance(stage1_tr, FlowDDPMStageTrainer) - else None, steps=validate_steps, + ddpm_steps=stage1_tr.ddpm_schedule.T + if isinstance(stage1_tr, FlowDDPMStageTrainer) + and stage1_tr.ddpm_schedule is not None + else 1000, sec_decoder=trainers["stage2"].sampling_model() if "stage2" in trainers else None, diff --git a/giant/validate.py b/giant/validate.py index 62a88df..ea8a34e 100644 --- a/giant/validate.py +++ b/giant/validate.py @@ -2,23 +2,12 @@ import numpy as np import torch from torch.utils.data import DataLoader -from giant.constants import K_MAX, LOCAL_TARGET_NAMES -from giant.sample import ( - sample_flow, - sample_ddpm, - sample_ddim, - sample_secondaries, - sample_wgan, - sample_secondaries_wgan, -) +from giant.constants import LOCAL_TARGET_NAMES +from giant.sample import resolve_n_sec, sample_stage1, sample_stage2 _SEC_PHYS_NAMES = ["log_mass", "charge"] -def _kw(steps: int | None) -> dict[str, int]: - return {} if steps is None else {"steps": steps} - - def _histogram_kl( p_samples: np.ndarray, q_samples: np.ndarray, bins: int = 50, eps: float = 1e-8 ) -> float: @@ -44,15 +33,44 @@ def _bincount_frac(x: np.ndarray, minlength: int) -> np.ndarray: return counts / total if total > 0 else counts +def _categorical_kl( + real_idx: np.ndarray, gen_idx: np.ndarray, n_classes: int, eps: float = 1e-8 +) -> float: + """KL(P_real || Q_gen) between two class-index samples over `n_classes` + categories, estimated from bincount fractions. NaN if either side has no + valid samples (mirrors `_histogram_kl`'s empty-input handling).""" + if len(real_idx) == 0 or len(gen_idx) == 0: + return float("nan") + p = _bincount_frac(real_idx, n_classes) + eps + q = _bincount_frac(gen_idx, n_classes) + eps + p /= p.sum() + q /= q.sum() + return float(np.sum(p * np.log(p / q))) + + +def _embedding_nearest_class( + vectors: torch.Tensor, emb_weight: torch.Tensor +) -> np.ndarray: + """Nearest row index (L1) of `vectors` (..., emb_dim) against `emb_weight` + (vocab, emb_dim) — same computation as + `giant.particles.decode_embedding_nearest`, but returning the raw class + index instead of a decoded PDG code: validate.py only needs a + real-vs-generated class-distribution comparison, not a rollout-usable + identity, so there's no need for the pdg_map inversion here.""" + flat = vectors.reshape(-1, vectors.size(-1)) + dist = (flat.unsqueeze(1) - emb_weight.detach().unsqueeze(0)).abs().sum(-1) + nearest = dist.argmin(dim=1) + return nearest.reshape(vectors.shape[:-1]).cpu().numpy() + + def validate_marginals( - model: torch.nn.Module, + stage1_model: torch.nn.Module, val_loader: DataLoader, - mode: str = "flow", - schedule=None, device: torch.device | None = None, n_batches: int | None = None, kl_bins: int = 50, - steps: int | None = None, + steps: int = 10, + ddpm_steps: int = 1000, sec_decoder: torch.nn.Module | None = None, ) -> dict[str, np.ndarray | float]: """Compare per-dimension marginals of generated vs. real steps. @@ -61,51 +79,61 @@ def validate_marginals( normalised space. `kl_divergence[j]` is KL(real || generated) for dimension j, estimated from a shared histogram over both samples. - `steps` overrides the number of sampler steps (flow ODE steps or DDIM - substeps); `None` keeps each sampler's own default. Unused in "ddpm" - mode, which always runs the full schedule. + Stage 1 is sampled via `giant.sample.sample_stage1`, which dispatches on + `stage1_model.generator_kind` — `steps`/`ddpm_steps` are forwarded but + only one of them is actually read, depending on that dispatch. - When `sec_decoder` is given, also validates Stage 2: n_sec distribution - (+ classification accuracy), predicted secondary physical-identity - (log_mass, charge) marginals, and per-slot energy-fraction marginals — - restricted to each side's own valid slots (real: `n_sec`; generated: the - Stage-1 head's argmax), since the two need not agree on how many slots - are valid. Compared directly in normalised space (no denormalising — - KL estimated from a shared per-sample histogram is invariant to a shared - affine rescaling of both sides). Adds {"n_sec_real", "n_sec_pred", - "n_sec_accuracy", "phys_real", "phys_generated", "phys_kl", - "energy_fraction_kl"} to the returned dict. + When `sec_decoder` is given, also validates Stage 2 via + `giant.sample.sample_stage2`/`resolve_n_sec` (generator- and + one-shot-vs-autoregressive-agnostic, docs/v0.3.0-design.md §10): n_sec + distribution (+ classification accuracy), per-slot energy-fraction + marginals, and a particle-type marginal whose shape depends on + `sec_decoder.particle_type_cfg["target"]` — restricted to each side's own + valid slots (real: `n_sec`; generated: the resolved `n_sec_pred`), since + the two need not agree on how many slots are valid. Adds {"n_sec_real", + "n_sec_pred", "n_sec_accuracy", "energy_fraction_kl"} plus, under + `target = "physical"`, {"phys_real", "phys_generated", "phys_kl"} + (continuous log_mass/charge marginals — v0.2 behaviour), or under + `target` in `("onehot", "embedding")`, {"type_class_real", + "type_class_gen", "type_class_kl"} (categorical class-index marginal: + argmax for "onehot", L1-nearest conditioning-embedding row for + "embedding" — see `_embedding_nearest_class`). Compared directly in + normalised space (no denormalising — KL estimated from a shared + per-sample histogram/bincount is invariant to a shared affine rescaling + of both sides). """ if device is None: - device = next(model.parameters()).device - model.eval() + device = next(stage1_model.parameters()).device + stage1_model.eval() if sec_decoder is not None: sec_decoder.eval() + k_max = sec_decoder.k_max if sec_decoder is not None else 0 + target = ( + sec_decoder.particle_type_cfg.get("target", "physical") + if sec_decoder is not None + else "physical" + ) + all_real, all_gen = [], [] all_n_sec_real, all_n_sec_pred = [], [] all_phys_real, all_phys_gen = [], [] - all_frac_real: list[list[np.ndarray]] = [[] for _ in range(K_MAX)] - all_frac_gen: list[list[np.ndarray]] = [[] for _ in range(K_MAX)] + all_type_class_real, all_type_class_gen = [], [] + all_frac_real: list[list[np.ndarray]] = [[] for _ in range(k_max)] + all_frac_gen: list[list[np.ndarray]] = [[] for _ in range(k_max)] for i, batch in enumerate(val_loader): if n_batches is not None and i >= n_batches: break - # Batch is (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx). - cond_cont, cond_cat, x1, n_sec, sec_cont, _proc_idx = batch + # Batch is (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, + # sec_type_idx). + cond_cont, cond_cat, x1, n_sec, sec_cont, _proc_idx, sec_type_idx = batch cond_cont = cond_cont.to(device) cond_cat = cond_cat.to(device) - if mode == "flow": - gen, n_sec_pred = sample_flow(model, cond_cont, cond_cat, **_kw(steps)) - elif mode == "ddpm": - gen, n_sec_pred = sample_ddpm(model, cond_cont, cond_cat, schedule) - elif mode == "wgan": - gen, n_sec_pred = sample_wgan(model, cond_cont, cond_cat) - else: - gen, n_sec_pred = sample_ddim( - model, cond_cont, cond_cat, schedule, **_kw(steps) - ) + gen, n_sec_pred = sample_stage1( + stage1_model, cond_cont, cond_cat, steps=steps, ddpm_steps=ddpm_steps + ) all_real.append(x1.numpy()) all_gen.append(gen.cpu().numpy()) @@ -113,37 +141,41 @@ def validate_marginals( if sec_decoder is None: continue + n_sec_pred = resolve_n_sec( + stage1_model, sec_decoder, cond_cont, cond_cat, gen, n_sec_pred + ) n_sec_pred_np = n_sec_pred.cpu().numpy() n_sec_np = n_sec.numpy() all_n_sec_real.append(n_sec_np) all_n_sec_pred.append(n_sec_pred_np) - real_valid = np.arange(K_MAX)[None, :] < n_sec_np[:, None] # (B, K_MAX) + real_valid = np.arange(k_max)[None, :] < n_sec_np[:, None] # (B, k_max) real_frac = 1.0 / (1.0 + np.exp(-sec_cont[:, :, 0].numpy().astype(np.float64))) - real_phys = sec_cont[:, :, 4:6].numpy() # (B, K_MAX, 2) [log_mass, charge] - if mode == "wgan": - sec_cont_pred, sec_phys_pred, sec_valid_pred = sample_secondaries_wgan( - sec_decoder, cond_cont, cond_cat, gen, n_sec_pred - ) - else: - sec_cont_pred, sec_phys_pred, sec_valid_pred = sample_secondaries( - sec_decoder, - cond_cont, - cond_cat, - gen, - n_sec_pred, - steps=steps if steps is not None else 10, - ) + sec_cont_pred, sec_type_pred, sec_valid_pred = sample_stage2( + sec_decoder, cond_cont, cond_cat, gen, n_sec_pred, steps=steps + ) gen_frac = 1.0 / ( 1.0 + np.exp(-sec_cont_pred[:, :, 0].cpu().numpy().astype(np.float64)) ) - gen_phys = sec_phys_pred.cpu().numpy() gen_valid = sec_valid_pred.cpu().numpy() - all_phys_real.append(real_phys[real_valid]) - all_phys_gen.append(gen_phys[gen_valid]) - for j in range(K_MAX): + if target == "physical": + real_phys = sec_cont[:, :, 4:6].numpy() # (B, k_max, 2) [log_mass, charge] + gen_phys = sec_type_pred.cpu().numpy() + all_phys_real.append(real_phys[real_valid]) + all_phys_gen.append(gen_phys[gen_valid]) + else: + sec_type_idx_np = sec_type_idx.numpy() + all_type_class_real.append(sec_type_idx_np[real_valid]) + if target == "onehot": + gen_class = sec_type_pred.argmax(dim=-1).cpu().numpy() + else: # "embedding" + emb_weight = sec_decoder.cond_enc.pdg_emb.weight + gen_class = _embedding_nearest_class(sec_type_pred, emb_weight) + all_type_class_gen.append(gen_class[gen_valid]) + + for j in range(k_max): all_frac_real[j].append(real_frac[real_valid[:, j], j]) all_frac_gen[j].append(gen_frac[gen_valid[:, j], j]) @@ -181,20 +213,8 @@ def validate_marginals( n_sec_real = np.concatenate(all_n_sec_real, axis=0) n_sec_pred_all = np.concatenate(all_n_sec_pred, axis=0) n_sec_accuracy = float((n_sec_real == n_sec_pred_all).mean()) - phys_real = np.concatenate(all_phys_real, axis=0) # (M, 2) - phys_gen = np.concatenate(all_phys_gen, axis=0) # (M, 2) - if len(phys_real) > 0 and len(phys_gen) > 0: - phys_kl = np.array( - [ - _histogram_kl(phys_real[:, j], phys_gen[:, j], bins=kl_bins) - for j in range(2) - ] - ) - else: - phys_kl = np.full(2, np.nan) - - energy_fraction_kl = np.full(K_MAX, np.nan) + energy_fraction_kl = np.full(k_max, np.nan) print( f"\n{'n_sec':<20} accuracy={n_sec_accuracy:.4f} " f"mean|Δ|={np.abs(n_sec_real - n_sec_pred_all).mean():.4f}" @@ -208,26 +228,12 @@ def validate_marginals( for v in range(max_n_sec): print(f"{v:<20} {real_n_sec_dist[v]:>10.4f} {gen_n_sec_dist[v]:>10.4f}") - print( - f"\n{'sec phys (normalised)':<20} {'real_mean':>10} {'gen_mean':>10} " - f"{'real_std':>10} {'gen_std':>10} {'KL(real||gen)':>14}" - ) - print("-" * 68) - for j, name in enumerate(_SEC_PHYS_NAMES): - r, g = phys_real[:, j], phys_gen[:, j] - if len(r) == 0 or len(g) == 0: - continue - print( - f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} " - f"{r.std():>10.4f} {g.std():>10.4f} {phys_kl[j]:>14.4f}" - ) - print( f"\n{'sec slot (energy frac.)':<24} {'real_mean':>10} {'gen_mean':>10} " f"{'real_std':>10} {'gen_std':>10} {'KL(real||gen)':>14}" ) print("-" * 90) - for j in range(K_MAX): + for j in range(k_max): r = np.concatenate(all_frac_real[j]) if all_frac_real[j] else np.array([]) g = np.concatenate(all_frac_gen[j]) if all_frac_gen[j] else np.array([]) if len(r) == 0 or len(g) == 0: @@ -244,10 +250,63 @@ def validate_marginals( "n_sec_real": n_sec_real, "n_sec_pred": n_sec_pred_all, "n_sec_accuracy": n_sec_accuracy, - "phys_real": phys_real, - "phys_generated": phys_gen, - "phys_kl": phys_kl, "energy_fraction_kl": energy_fraction_kl, } ) + + if target == "physical": + phys_real = np.concatenate(all_phys_real, axis=0) # (M, 2) + phys_gen = np.concatenate(all_phys_gen, axis=0) # (M, 2) + + if len(phys_real) > 0 and len(phys_gen) > 0: + phys_kl = np.array( + [ + _histogram_kl(phys_real[:, j], phys_gen[:, j], bins=kl_bins) + for j in range(2) + ] + ) + else: + phys_kl = np.full(2, np.nan) + + print( + f"\n{'sec phys (normalised)':<20} {'real_mean':>10} {'gen_mean':>10} " + f"{'real_std':>10} {'gen_std':>10} {'KL(real||gen)':>14}" + ) + print("-" * 68) + for j, name in enumerate(_SEC_PHYS_NAMES): + r, g = phys_real[:, j], phys_gen[:, j] + if len(r) == 0 or len(g) == 0: + continue + print( + f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} " + f"{r.std():>10.4f} {g.std():>10.4f} {phys_kl[j]:>14.4f}" + ) + + result.update( + {"phys_real": phys_real, "phys_generated": phys_gen, "phys_kl": phys_kl} + ) + else: + type_class_real = np.concatenate(all_type_class_real, axis=0) + type_class_gen = np.concatenate(all_type_class_gen, axis=0) + n_classes = ( + sec_decoder.type_dim + if target == "onehot" + else sec_decoder.cond_enc.pdg_emb.weight.size(0) + ) + type_class_kl = _categorical_kl(type_class_real, type_class_gen, n_classes) + + print( + f"\n{'sec type class (' + target + ')':<24} " + f"n={len(type_class_real)}/{len(type_class_gen)} " + f"KL(real||gen)={type_class_kl:.4f}" + ) + + result.update( + { + "type_class_real": type_class_real, + "type_class_gen": type_class_gen, + "type_class_kl": type_class_kl, + } + ) + return result diff --git a/scripts/dwarf.py b/scripts/dwarf.py index 200b1ec..311e384 100644 --- a/scripts/dwarf.py +++ b/scripts/dwarf.py @@ -522,10 +522,21 @@ def warm_cache( "--seed", "-s", help="Must match the `giant train` run(s) to warm for" ), ] = 0, - conditioning: Annotated[ + particle_conditioning: Annotated[ Conditioning, typer.Option( - "--conditioning", help="Must match the `giant train` run(s) to warm for" + "--particle-conditioning", + help="Must match the `giant train` run(s)' conditioning.particle.type " + "to warm for", + ), + ] = Conditioning.physical, + material_conditioning: Annotated[ + Conditioning, + typer.Option( + "--material-conditioning", + help="Must match the `giant train` run(s)' conditioning.material.type " + "to warm for — independent of --particle-conditioning " + "(docs/v0.3.0-design.md §3.1: the two axes may differ)", ), ] = Conditioning.physical, router: Annotated[ @@ -552,15 +563,17 @@ def warm_cache( """Precompute `giant train`'s setup-stage sidecar for `data` ahead of time. Warms the vocab maps, event-id split index, and the normalizer entry for - the given --val-fraction/--seed/--conditioning, so a later `giant train` - run (or a `dwarf hparam-scan` sweep, which shares one such entry across - every run) skips straight to training. See giant/data/setup_cache.py. + the given --val-fraction/--seed/--particle-conditioning/ + --material-conditioning, so a later `giant train` run (or a `dwarf + hparam-scan` sweep, which shares one such entry across every run) skips + straight to training. See giant/data/setup_cache.py. """ run_warm_setup_cache( data=str(data), val_fraction=val_fraction, seed=seed, - conditioning=conditioning.value, + particle_conditioning=particle_conditioning.value, + material_conditioning=material_conditioning.value, router_enabled=router, router_type=router_type, n_experts=n_experts, diff --git a/scripts/warm_setup_cache.py b/scripts/warm_setup_cache.py index 030529b..115463c 100644 --- a/scripts/warm_setup_cache.py +++ b/scripts/warm_setup_cache.py @@ -9,6 +9,7 @@ for the sidecar itself. from pathlib import Path +from giant.constants import K_MAX from giant.pipeline import run_setup_stage @@ -16,7 +17,8 @@ def run_warm_setup_cache( data: str, val_fraction: float = 0.1, seed: int = 0, - conditioning: str = "physical", + particle_conditioning: str = "physical", + material_conditioning: str = "physical", router_enabled: bool = False, router_type: str = "energy", n_experts: int = 4, @@ -25,10 +27,12 @@ def run_warm_setup_cache( ) -> None: """Populate (or refresh) the setup cache sidecar for `data`. - `val_fraction`/`seed`/`conditioning` select the normalizer cache entry + `val_fraction`/`seed`/`particle_conditioning`/`material_conditioning` + select the normalizer cache entry (`giant.data.setup_cache.normalizer_key`) — pass the same values a later - `giant train` invocation will use so it hits this warmed entry. - `router_enabled`/`router_type`/`n_experts` only matter for + `giant train` invocation will use so it hits this warmed entry. The two + conditioning axes are independent (docs/v0.3.0-design.md §3.1) and may + differ. `router_enabled`/`router_type`/`n_experts` only matter for `router_type == "process"` (warms that `n_experts`'s process map); the energy-router quantile summary is always collected regardless, so a later `--router-type energy` run never needs to rescan just to seed @@ -40,16 +44,17 @@ def run_warm_setup_cache( "n_experts": n_experts, } # A minimal v0.3 cfg — only the keys run_setup_stage actually reads - # (conditioning.particle.type, stage{1,2}_model.router). This CLI only - # ever configures one router (matching today's single --router-type - # flag), so it's placed on stage1_model; stage2_model's stays disabled. + # (conditioning.{particle,material}.type, stage{1,2}_model.router). This + # CLI only ever configures one router (matching today's single + # --router-type flag), so it's placed on stage1_model; stage2_model's + # stays disabled. cfg = { "conditioning": { - "particle": {"type": conditioning}, - "material": {"type": conditioning}, + "particle": {"type": particle_conditioning}, + "material": {"type": material_conditioning}, }, "stage1_model": {"router": router_cfg}, - "stage2_model": {"router": {"enabled": False}}, + "stage2_model": {"router": {"enabled": False}, "k_max": K_MAX}, } run_setup_stage( Path(data), diff --git a/tests/test_cli_predict.py b/tests/test_cli_predict.py index 1ee8af6..a6f74fb 100644 --- a/tests/test_cli_predict.py +++ b/tests/test_cli_predict.py @@ -1,73 +1,14 @@ import uuid -import pytest -import typer import yaml from giant.cli import ( _CEPH_PREDICTIONS, - _check_conditioning_onehot_support, _resolve_prediction_output, _write_prediction_ref, ) -# --------------------------------------------------------------------------- -# _check_conditioning_onehot_support -# --------------------------------------------------------------------------- - - -def _nested_model_cfg( - particle_type="physical", material_type="physical", target="physical" -): - return { - "conditioning": { - "particle": {"type": particle_type, "emb_dim": 8}, - "material": {"type": material_type, "emb_dim": 8}, - }, - "stage2_model": {"particle_type": {"target": target}}, - } - - -def test_check_conditioning_onehot_support_allows_physical(): - _check_conditioning_onehot_support(_nested_model_cfg(), "predict") # no raise - - -def test_check_conditioning_onehot_support_rejects_onehot_particle_conditioning(): - cfg = _nested_model_cfg(particle_type="onehot") - with pytest.raises(typer.Exit): - _check_conditioning_onehot_support(cfg, "predict") - - -def test_check_conditioning_onehot_support_rejects_onehot_material_conditioning(): - cfg = _nested_model_cfg(material_type="onehot") - with pytest.raises(typer.Exit): - _check_conditioning_onehot_support(cfg, "rollout") - - -def test_check_conditioning_onehot_support_allows_onehot_particle_type_target(): - """stage2_model.particle_type.target="onehot" is implemented (v0.3.0 - step 6, giant.rollout.decode_secondary_identity) — it's a separate axis - from conditioning.particle.type, which this guard doesn't gate at all.""" - cfg = _nested_model_cfg(target="onehot") - _check_conditioning_onehot_support(cfg, "predict") # no raise - - -def test_check_conditioning_onehot_support_allows_embedding_particle_type_target(): - cfg = _nested_model_cfg( - particle_type="embedding", material_type="embedding", target="embedding" - ) - _check_conditioning_onehot_support(cfg, "predict") # no raise - - -def test_check_conditioning_onehot_support_is_noop_for_v02_flat_model_config(): - """A v0.2 checkpoint's flat model_config has conditioning as a plain - string, not a dict — never onehot, so this must be a silent no-op rather - than crash on `.get("particle")` against a string.""" - cfg = {"conditioning": "embedding", "mode": "flow"} - _check_conditioning_onehot_support(cfg, "predict") # no raise - - # --------------------------------------------------------------------------- # _resolve_prediction_output # --------------------------------------------------------------------------- diff --git a/tests/test_cli_train_overrides.py b/tests/test_cli_train_overrides.py new file mode 100644 index 0000000..2341b69 --- /dev/null +++ b/tests/test_cli_train_overrides.py @@ -0,0 +1,97 @@ +"""Tests for `giant train`'s stage-prefixed CLI flags (docs/v0.3.0-design.md +decision 7 / docs/v0.3.0-followups.md item 2): --stage1-*/--stage2-* must +independently override each stage's config block, and must take precedence +over the older shared flags (--mode/--hidden-dim/--n-critic/... ) that still +apply the same value to both stages for backward compatibility.""" + +from __future__ import annotations + +from pathlib import Path + +from typer.testing import CliRunner + +import giant.cli as cli + +runner = CliRunner() + + +def _invoke_and_capture_cfg(monkeypatch, tmp_path: Path, args: list[str]) -> dict: + captured: dict = {} + + def _fake_run_train_job(*, data, cfg, out_dir, **kwargs): + captured["cfg"] = cfg + + monkeypatch.setattr(cli, "run_train_job", _fake_run_train_job) + + result = runner.invoke( + cli.app, + ["train", "dummy.parquet", "--out", str(tmp_path / "run")] + args, + ) + assert result.exit_code == 0, result.output + return captured["cfg"] + + +def test_stage_prefixed_generator_overrides_shared_mode(monkeypatch, tmp_path): + cfg = _invoke_and_capture_cfg( + monkeypatch, + tmp_path, + ["--mode", "wgan", "--stage1-generator", "flow"], + ) + assert cfg["stage1_model"]["generator"] == "flow" + assert cfg["stage2_model"]["generator"] == "wgan" + + +def test_stage2_only_knobs(monkeypatch, tmp_path): + cfg = _invoke_and_capture_cfg( + monkeypatch, + tmp_path, + [ + "--stage2-decoder", + "one_shot", + "--stage2-k-max", + "8", + "--stage2-hidden-dim", + "32", + "--stage2-context-dim", + "16", + "--stage2-stage1-context", + "sampled", + ], + ) + assert cfg["stage2_model"]["decoder"] == "one_shot" + assert cfg["stage2_model"]["k_max"] == 8 + assert cfg["stage2_model"]["hidden_dim"] == 32 + assert cfg["stage2_model"]["context_dim"] == 16 + assert cfg["stage2_model"]["stage1_context"] == "sampled" + # untouched stage1 defaults + assert cfg["stage1_model"]["hidden_dim"] == 256 + + +def test_stage1_hidden_dim_flag_overrides_legacy_hidden_dim_flag(monkeypatch, tmp_path): + cfg = _invoke_and_capture_cfg( + monkeypatch, + tmp_path, + ["--hidden-dim", "64", "--stage1-hidden-dim", "128"], + ) + assert cfg["stage1_model"]["hidden_dim"] == 128 + + +def test_wgan_knobs_split_per_stage(monkeypatch, tmp_path): + cfg = _invoke_and_capture_cfg( + monkeypatch, + tmp_path, + [ + "--mode", + "wgan", + "--n-critic", + "5", + "--stage1-n-critic", + "3", + "--stage2-gp-weight", + "2.5", + ], + ) + assert cfg["stage1_model"]["wgan"]["n_critic"] == 3 + assert cfg["stage1_model"]["wgan"]["gp_weight"] == 10.0 + assert cfg["stage2_model"]["wgan"]["n_critic"] == 5 + assert cfg["stage2_model"]["wgan"]["gp_weight"] == 2.5 diff --git a/tests/test_config.py b/tests/test_config.py index b57b5cf..ca50de6 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -584,21 +584,19 @@ def test_validate_config_embedding_target_passes_with_embedding_conditioning(): gconfig.validate_config(cfg) # must not raise -def test_validate_config_mixed_particle_material_conditioning_not_supported(): - """The data pipeline doesn't support mixed conditioning types yet, even - though ConditionEncoder itself already can (docs/v0.3.0-design.md §3.1 - vs. giant/data/transforms.py's still-single conditioning param).""" +def test_validate_config_mixed_particle_material_conditioning_is_valid(): + """docs/v0.3.0-design.md §3.1: the particle and material conditioning + axes are configured independently and may mix freely — e.g. material + "physical" with particle "embedding" — and the data pipeline + (giant/data/transforms.py) now implements that end-to-end, so + validate_config must not reject it.""" cfg = _cfg_with( **{ "conditioning.particle.type": "physical", "conditioning.material.type": "embedding", } ) - try: - gconfig.validate_config(cfg) - assert False, "expected ValueError" - except ValueError as e: - assert "mixed" in str(e) or "conditioning.material.type" in str(e) + gconfig.validate_config(cfg) # must not raise def test_validate_config_pdg_router_incompatible_with_physical_conditioning(): @@ -639,6 +637,49 @@ def test_validate_config_stop_token_not_implemented(): assert "stop_token" in str(e) +def test_validate_config_n_sec_truth_rejected_for_rollout_capable_checkpoint(): + """docs/v0.3.0-design.md §9: 'n_sec.mode = "truth" is invalid for a + rollout-capable checkpoint' — both stages active means giant rollout + could load this checkpoint, but 'truth' has no ground truth to draw + n_sec from at rollout time.""" + cfg = _cfg_with( + **{ + "stage2_model.n_sec.mode": "truth", + "stage1_model.active": True, + "stage2_model.active": True, + } + ) + try: + gconfig.validate_config(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "n_sec.mode" in str(e) and "truth" in str(e) + + +def test_validate_config_n_sec_truth_allowed_for_stage2_only_checkpoint(): + """'truth' is exactly the standalone stage-2 evaluation mode the design + doc carves out — stage1_model.active = false must still pass.""" + cfg = _cfg_with( + **{ + "stage2_model.n_sec.mode": "truth", + "stage1_model.active": False, + "stage2_model.active": True, + } + ) + gconfig.validate_config(cfg) # must not raise + + +def test_validate_config_n_sec_truth_allowed_when_stage2_inactive(): + cfg = _cfg_with( + **{ + "stage2_model.n_sec.mode": "truth", + "stage1_model.active": True, + "stage2_model.active": False, + } + ) + gconfig.validate_config(cfg) # must not raise + + def test_validate_config_ar_default_markov_always_passes(): """DEFAULT_CONFIG already has decoder='autoregressive', history='markov', teacher_forcing='always' — must not raise (v0.3.0 diff --git a/tests/test_dataset.py b/tests/test_dataset.py index e311dae..5d2e71e 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -126,7 +126,8 @@ def test_streaming_dataset_offsets_colliding_event_ids_across_files(tmp_path): target_normalizer=tgt_norm, batch_size=4, shuffle=False, - conditioning="embedding", + particle_conditioning="embedding", + material_conditioning="embedding", ) return sum(len(batch[0]) for batch in ds) diff --git a/tests/test_dwarf.py b/tests/test_dwarf.py index 14b2673..767828e 100644 --- a/tests/test_dwarf.py +++ b/tests/test_dwarf.py @@ -103,7 +103,7 @@ def test_warm_cache_writes_sidecar(tmp_path): assert loaded is not None assert loaded.vocab is not None assert loaded.event_index is not None - assert "valfrac=0.1_seed=0_cond=physical" in loaded.normalizers + assert "valfrac=0.1_seed=0_pcond=physical_mcond=physical" in loaded.normalizers def test_warm_cache_second_run_hits_cache(tmp_path): @@ -167,5 +167,5 @@ def test_warm_cache_different_val_fraction_is_separate_entry(tmp_path): assert "fitting normalizer (streaming)" in result.output loaded = setup_cache.load(data, [data]) assert loaded is not None - assert "valfrac=0.1_seed=0_cond=physical" in loaded.normalizers - assert "valfrac=0.3_seed=0_cond=physical" in loaded.normalizers + assert "valfrac=0.1_seed=0_pcond=physical_mcond=physical" in loaded.normalizers + assert "valfrac=0.3_seed=0_pcond=physical_mcond=physical" in loaded.normalizers diff --git a/tests/test_flow.py b/tests/test_flow.py index f1276f0..aae4a93 100644 --- a/tests/test_flow.py +++ b/tests/test_flow.py @@ -52,7 +52,7 @@ def test_sample_flow_shape(): cond_cat = torch.zeros(B, 2, dtype=torch.long) sample, n_sec = sample_flow(_small_model(), cond_cont, cond_cat, steps=5) assert sample.shape == (B, 9) - assert n_sec.shape == (B,) + assert n_sec is not None and n_sec.shape == (B,) def test_ddpm_loss_nonneg(): @@ -69,4 +69,4 @@ def test_sample_ddim_shape(): cond_cat = torch.zeros(B, 2, dtype=torch.long) sample, n_sec = sample_ddim(_small_model(), cond_cont, cond_cat, schedule, steps=5) assert sample.shape == (B, 9) - assert n_sec.shape == (B,) + assert n_sec is not None and n_sec.shape == (B,) diff --git a/tests/test_migration_v02_v03.py b/tests/test_migration_v02_v03.py index 488bf37..6c5d10c 100644 --- a/tests/test_migration_v02_v03.py +++ b/tests/test_migration_v02_v03.py @@ -198,6 +198,56 @@ def test_migrate_legacy_model_config_shape(): assert migrated["stage2_model"]["decoder"] == "one_shot" +def test_migrate_legacy_model_config_nonzero_expert_dims_raises(): + """docs/v0.3.0-followups.md item 8 regression: a v0.2 checkpoint's + model_config carrying a non-default expert_hidden_dim/expert_n_blocks + must fail loudly through this path too (§4.2) — not just + giant.config.migrate_config's parallel TOML-load path. Silently dropping + these keys (build_router's kwarg filtering) would resize the experts + instead of refusing.""" + legacy_cfg = _legacy_model_config(mode="flow", conditioning="physical") + legacy_cfg["router"] = { + "enabled": True, + "expert_hidden_dim": 128, + "expert_n_blocks": 0, + } + try: + net._migrate_legacy_model_config(legacy_cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "expert_hidden_dim" in str(e) + + +def test_migrate_legacy_model_config_zero_expert_dims_dropped_silently(): + legacy_cfg = _legacy_model_config(mode="flow", conditioning="physical") + legacy_cfg["router"] = { + "enabled": True, + "expert_hidden_dim": 0, + "expert_n_blocks": 0, + } + migrated = net._migrate_legacy_model_config(legacy_cfg) + assert "expert_hidden_dim" not in migrated["stage1_model"]["router"] + assert "expert_n_blocks" not in migrated["stage1_model"]["router"] + assert "expert_hidden_dim" not in migrated["stage2_model"]["router"] + assert "expert_n_blocks" not in migrated["stage2_model"]["router"] + + +def test_build_models_with_legacy_config_nonzero_expert_dims_raises(): + """The same check must also fire through the actual caller, + build_models, not just the internal helper directly.""" + legacy_cfg = _legacy_model_config(mode="flow", conditioning="physical") + legacy_cfg["router"] = { + "enabled": True, + "expert_hidden_dim": 128, + "expert_n_blocks": 0, + } + try: + net.build_models(legacy_cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "expert_hidden_dim" in str(e) + + def test_build_models_accepts_new_nested_shape_unchanged(): """A dict that already has a 'stage1_model' key (the new shape) is passed through build_models without going through the legacy migration diff --git a/tests/test_network.py b/tests/test_network.py index f3c5293..0a254f6 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -1,5 +1,8 @@ +import copy + import pytest import torch +from giant import config as gconfig from giant.constants import CONT_SLOT_DIM, COND_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM from giant.model.network import ( AttentionHistory, @@ -9,6 +12,7 @@ from giant.model.network import ( Stage1Model, Stage2Autoregressive, Stage2OneShot, + build_models, cat_col_layout, stage2_trunk_sec_dim, stage2_type_dim, @@ -628,3 +632,52 @@ def test_stage2_autoregressive_history_step_matches_parallel_history_encoder(): def test_stage2_autoregressive_init_history_cache_is_none_for_markov(): model = _build_stage2_ar("physical", "wgan", history="markov") assert model.init_history_cache() is None + + +# ── build_models: conditioning.share_stages ───────────────────────────────── + + +def _minimal_model_config(share_stages: bool) -> dict: + cfg = copy.deepcopy(gconfig.DEFAULT_CONFIG) + cfg["conditioning"]["share_stages"] = share_stages + cfg["conditioning"]["particle"]["emb_dim"] = 4 + cfg["conditioning"]["material"]["emb_dim"] = 4 + cfg["stage1_model"].update({"hidden_dim": 8, "n_res_blocks": 1}) + cfg["stage2_model"].update({"hidden_dim": 8, "n_res_blocks": 1, "k_max": 3}) + return { + "pdg_vocab": 3, + "mat_vocab": 2, + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], + } + + +def test_build_models_share_stages_true_shares_condition_encoder_instance(): + built = build_models(_minimal_model_config(share_stages=True)) + stage1, stage2 = built["stage1"], built["stage2"] + assert stage1 is not None and stage2 is not None + assert stage1.cond_enc is stage2.cond_enc + + +def test_build_models_share_stages_false_builds_independent_condition_encoders(): + built = build_models(_minimal_model_config(share_stages=False)) + stage1, stage2 = built["stage1"], built["stage2"] + assert stage1 is not None and stage2 is not None + assert stage1.cond_enc is not stage2.cond_enc + + +def test_build_models_share_stages_true_shared_params_are_in_both_stage_parameter_lists(): + """The shared encoder's parameters must actually appear in both stages' + own `.parameters()` — that's what makes each stage's independent + optimizer include (and update) them, which is the actual mechanism behind + "shared weights, forced common representation" (docs/v0.3.0-design.md + §3.1), not just object identity on `.cond_enc`.""" + built = build_models(_minimal_model_config(share_stages=True)) + stage1, stage2 = built["stage1"], built["stage2"] + assert stage1 is not None and stage2 is not None + + shared_ids = {id(p) for p in stage1.cond_enc.parameters()} + assert shared_ids + assert shared_ids <= {id(p) for p in stage1.parameters()} + assert shared_ids <= {id(p) for p in stage2.parameters()} diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index f4c093c..f85beca 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -7,6 +7,7 @@ import torch from giant import config as gconfig from giant.data import setup_cache +from giant.data.transforms import Normalizer from giant.pipeline import run_train_job @@ -235,6 +236,74 @@ def test_run_train_job_new_val_fraction_is_partial_hit(tmp_path, data, monkeypat assert "fitting normalizer (streaming)" in joined +def test_run_train_job_custom_k_max_end_to_end(tmp_path, data): + """docs/v0.3.0-followups.md item 3 regression: stage2_model.k_max other + than the K_MAX module constant's default (15) must not produce a shape + mismatch between the data pipeline (loader.py/transforms.py padding) and + the model (network.py's trunks, sized from this same config value).""" + cfg = _tiny_cfg() + cfg["stage2_model"]["k_max"] = 3 + _run(data, tmp_path / "out", cfg=cfg) + ckpt = torch.load(tmp_path / "out" / "last.pt", weights_only=False) + assert ckpt["model_config"]["stage2_model"]["k_max"] == 3 + + +def test_run_train_job_mixed_particle_material_conditioning_end_to_end(tmp_path, data): + """docs/v0.3.0-followups.md item 4 regression: conditioning.particle.type + and conditioning.material.type are configured independently and may mix + freely (docs/v0.3.0-design.md §3.1) — e.g. particle "embedding" with + material "physical" — end-to-end through the real data pipeline, not + just accepted by validate_config.""" + cfg = _tiny_cfg() + cfg["conditioning"]["particle"]["type"] = "embedding" + cfg["conditioning"]["material"]["type"] = "physical" + _run(data, tmp_path / "out", cfg=cfg) + ckpt = torch.load(tmp_path / "out" / "last.pt", weights_only=False) + cond_cfg = ckpt["model_config"]["conditioning"] + assert cond_cfg["particle"]["type"] == "embedding" + assert cond_cfg["material"]["type"] == "physical" + + cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) + # Particle block ([COND_DIM_BASE:COND_DIM_BASE+PARTICLE_PHYS_DIM]) stays + # unfitted (mean=0/std=1) since "embedding" never computes real values + # for it; the material block is fit for real under "physical". + from giant.constants import COND_DIM_BASE, PARTICLE_PHYS_DIM + + assert cond_norm.mean is not None and cond_norm.std is not None + np.testing.assert_allclose( + cond_norm.mean[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 0.0 + ) + np.testing.assert_allclose( + cond_norm.std[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 1.0 + ) + material_std = cond_norm.std[COND_DIM_BASE + PARTICLE_PHYS_DIM :] + assert np.all(material_std > 0) and not np.allclose(material_std, 1.0) + + +def test_run_train_job_share_stages_end_to_end(tmp_path, data): + """docs/v0.3.0-followups.md item 5 regression: conditioning.share_stages + = true must actually train (not raise NotImplementedError), and the + resulting checkpoint's two stages must reload into a single shared + ConditionEncoder instance rather than two independent ones.""" + from giant.model.network import build_models + + cfg = _tiny_cfg() + cfg["conditioning"]["share_stages"] = True + _run(data, tmp_path / "out", cfg=cfg) + ckpt = torch.load(tmp_path / "out" / "last.pt", weights_only=False) + assert ckpt["model_config"]["conditioning"]["share_stages"] is True + + built = build_models(ckpt["model_config"]) + stage1, stage2 = built["stage1"], built["stage2"] + assert stage1 is not None and stage2 is not None + assert stage1.cond_enc is stage2.cond_enc + + stage1.load_state_dict(ckpt["model"]) + stage2.load_state_dict(ckpt["sec_decoder"]) + for p1, p2 in zip(stage1.cond_enc.parameters(), stage2.cond_enc.parameters()): + assert torch.equal(p1, p2) + + def test_run_train_job_matches_uncached_output(tmp_path, data): _run(data, tmp_path / "uncached", cache_setup=False) _run(data, tmp_path / "cached1", cache_setup=True) diff --git a/tests/test_rollout.py b/tests/test_rollout.py index f03eab8..7c230b2 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -111,7 +111,8 @@ def _run( batch_size=128, max_tracks_per_event=max_tracks_per_event, escape_threshold=escape_threshold, - conditioning=conditioning, + particle_conditioning=conditioning, + material_conditioning=conditioning, ) @@ -172,7 +173,7 @@ def test_seed_frontier_embedding_mode_skips_unresolvable_pdg_lookup(): frontier construction — mass/charge are simply zero-filled, unused.""" seeds = _seeds(3) seeds["pdg"] = np.full(3, 999999999, dtype=np.int64) - fr, _counts = make_seed_frontier(**seeds, conditioning="embedding") + fr, _counts = make_seed_frontier(**seeds, particle_conditioning="embedding") np.testing.assert_array_equal(fr["mass"], 0.0) np.testing.assert_array_equal(fr["charge"], 0.0) @@ -384,25 +385,43 @@ def _models_v3( noise_dim=8, ) particle_type_cfg = {"target": target} - common = dict( - pdg_vocab=3, - mat_vocab=2, - particle_cfg=particle_cfg, - material_cfg=material_cfg, - hidden_dim=32, - n_res_blocks=2, - generator=generator2, - time_dim=16, - noise_dim=8, - k_max=k_max, - particle_type_cfg=particle_type_cfg, - build_n_sec_head=stage2_has_n_sec_head, - ) + # Explicit kwargs rather than a shared **common dict: a dict() call whose + # values have heterogeneous types (str/int/dict/bool) widens under static + # analysis to dict[str, ], which then makes every constructor + # keyword not itself part of that union (router, cond_enc, ...) look like + # a type mismatch to `ty` even though every actual value passed is fine. if decoder == "one_shot": sec_dim = stage2_trunk_sec_dim(particle_type_cfg, generator2, k_max, emb_dim) - s2 = Stage2OneShot(sec_dim=sec_dim, **common) + s2 = Stage2OneShot( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator2, + time_dim=16, + noise_dim=8, + k_max=k_max, + particle_type_cfg=particle_type_cfg, + build_n_sec_head=stage2_has_n_sec_head, + sec_dim=sec_dim, + ) else: - s2 = Stage2Autoregressive(**common) + s2 = Stage2Autoregressive( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + generator=generator2, + time_dim=16, + noise_dim=8, + k_max=k_max, + particle_type_cfg=particle_type_cfg, + build_n_sec_head=stage2_has_n_sec_head, + ) return s1.eval(), s2.eval() @@ -440,7 +459,8 @@ def _run_v3( batch_size=128, max_tracks_per_event=max_tracks_per_event, escape_threshold=escape_threshold, - conditioning=conditioning, + particle_conditioning=conditioning, + material_conditioning=conditioning, pdg_topn_map=pdg_topn_map, other_policy=other_policy, seed=seed, @@ -535,6 +555,88 @@ def test_rollout_onehot_target_missing_topn_map_raises(fake_material_props): _run_v3(s1, s2, pdg_topn_map=None) +# --- conditioning.{particle,material}.type = "onehot" (docs/v0.3.0-followups.md +# item 7) — a separate axis from stage2_model.particle_type.target above: this +# is what feeds cond_cat's extra top-N columns for ConditionEncoder's own +# "onehot" mode, not the secondary-species decode. --------------------------- + +COND_PDG_TOPN_MAP = TopNMap(class_map=dict(PDG_MAP), other_members={}) +COND_MAT_TOPN_MAP = TopNMap(class_map={"G4_AIR": 0, "G4_PbWO4": 1}, other_members={}) + + +def _onehot_conditioning_models(): + particle_cfg = {"type": "onehot", "emb_dim": len(PDG_MAP), "n_layers": 1} + material_cfg = {"type": "onehot", "emb_dim": len(MAT_MAP), "n_layers": 1} + s1 = Stage1Model( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + ) + s2 = Stage2OneShot( + pdg_vocab=3, + mat_vocab=2, + particle_cfg=particle_cfg, + material_cfg=material_cfg, + hidden_dim=32, + n_res_blocks=2, + sec_dim=stage2_trunk_sec_dim({"target": "physical"}, "flow", K_MAX, 3), + generator="flow", + time_dim=16, + ) + return s1.eval(), s2.eval() + + +def _run_onehot_conditioning( + pdg_topn_map=COND_PDG_TOPN_MAP, mat_topn_map=COND_MAT_TOPN_MAP +): + s1, s2 = _onehot_conditioning_models() + cond, tgt, sec_phys = _norms() + return rollout( + s1, + s2, + _oracle(), + _seeds(), + cond, + tgt, + sec_phys, + PDG_MAP, + MAT_MAP, + energy_cutoff=1.0, + max_steps=30, + steps=4, + batch_size=128, + max_tracks_per_event=300, + escape_threshold=1e9, + particle_conditioning="onehot", + material_conditioning="onehot", + pdg_topn_map=pdg_topn_map, + mat_topn_map=mat_topn_map, + ) + + +def test_rollout_conditioning_onehot_end_to_end(fake_material_props): + rec = _run_onehot_conditioning() + assert len(rec["event_id"]) > 0 + assert set(rec["event_id"].tolist()) == set(range(6)) + + +def test_rollout_conditioning_onehot_particle_missing_topn_map_raises( + fake_material_props, +): + with pytest.raises(RuntimeError, match="pdg_topn_map"): + _run_onehot_conditioning(pdg_topn_map=None) + + +def test_rollout_conditioning_onehot_material_missing_topn_map_raises( + fake_material_props, +): + with pytest.raises(RuntimeError, match="mat_topn_map"): + _run_onehot_conditioning(mat_topn_map=None) + + @pytest.mark.parametrize("decoder", ["one_shot", "autoregressive"]) def test_rollout_embedding_target_end_to_end(decoder): """particle_type.target="embedding" L1-snaps to the nearest row of the diff --git a/tests/test_sample.py b/tests/test_sample.py index cb8bdfd..ff52610 100644 --- a/tests/test_sample.py +++ b/tests/test_sample.py @@ -150,7 +150,7 @@ def test_sample_flow_returns_n_sec_for_legacy_stage1(): ) cond_cont, cond_cat = _cond(5) _, n_sec = sample_flow(model, cond_cont, cond_cat, steps=2) - assert n_sec.shape == (5,) + assert n_sec is not None and n_sec.shape == (5,) # ── Stage2OneShot: non-"physical" particle_type.target ────────────────────── diff --git a/tests/test_transforms.py b/tests/test_transforms.py index a2e19c2..04ce58d 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -426,7 +426,13 @@ def test_build_features_embedding_mode_zero_fills_physical_columns(): data = _minimal_step_data(3) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} - cond_cont, *_ = build_features(data, pdg_map, mat_map, conditioning="embedding") + cond_cont, *_ = build_features( + data, + pdg_map, + mat_map, + particle_conditioning="embedding", + material_conditioning="embedding", + ) assert cond_cont.shape[1] == COND_DIM np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE:], 0.0) @@ -438,7 +444,13 @@ def test_build_features_physical_mode_shape_and_values(fake_material_props): data = _minimal_step_data(3) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} - cond_cont, *_ = build_features(data, pdg_map, mat_map, conditioning="physical") + cond_cont, *_ = build_features( + data, + pdg_map, + mat_map, + particle_conditioning="physical", + material_conditioning="physical", + ) assert cond_cont.shape[1] == COND_DIM mass, charge = particle_mass_charge(11) @@ -461,7 +473,13 @@ def test_build_features_physical_mode_unfilled_material_raises(): pdg_map, mat_map = {11: 0}, {"G4_LYSO": 0} with pytest.raises(MaterialPropertiesNotFilledError): - build_features(data, pdg_map, mat_map, conditioning="physical") + build_features( + data, + pdg_map, + mat_map, + particle_conditioning="physical", + material_conditioning="physical", + ) def test_build_cond_features_mass_charge_override(fake_material_props): @@ -474,7 +492,13 @@ def test_build_cond_features_mass_charge_override(fake_material_props): data["charge"] = np.array([2.0, -2.0], dtype=np.float32) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} - cond_cont, _ = build_cond_features(data, pdg_map, mat_map, conditioning="physical") + cond_cont, _ = build_cond_features( + data, + pdg_map, + mat_map, + particle_conditioning="physical", + material_conditioning="physical", + ) np.testing.assert_allclose( cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0])) @@ -494,7 +518,12 @@ def test_build_cond_features_pads_legacy_normalizer_in_embedding_mode(): legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32) cond_cont, _ = build_cond_features( - data, pdg_map, mat_map, cond_normalizer=legacy_norm, conditioning="embedding" + data, + pdg_map, + mat_map, + cond_normalizer=legacy_norm, + particle_conditioning="embedding", + material_conditioning="embedding", ) assert cond_cont.shape[-1] == COND_DIM @@ -521,7 +550,8 @@ def test_build_cond_features_rejects_legacy_normalizer_in_physical_mode( pdg_map, mat_map, cond_normalizer=legacy_norm, - conditioning="physical", + particle_conditioning="physical", + material_conditioning="physical", ) @@ -610,13 +640,23 @@ def test_build_cond_features_physical_mode_tolerates_out_of_vocab_pdg_and_materi } cond_cont, cond_cat = build_cond_features( - data, pdg_map, mat_map, conditioning="physical" + data, + pdg_map, + mat_map, + particle_conditioning="physical", + material_conditioning="physical", ) assert cond_cont.shape[-1] == COND_DIM np.testing.assert_array_equal(cond_cat, [[0, 0]]) # dummy indices, no raise with pytest.raises(KeyError): - build_cond_features(data, pdg_map, mat_map, conditioning="embedding") + build_cond_features( + data, + pdg_map, + mat_map, + particle_conditioning="embedding", + material_conditioning="embedding", + ) # ── _WelfordAccumulator ────────────────────────────────────────────────────── diff --git a/tests/test_validate.py b/tests/test_validate.py index 61fa72f..533c933 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -1,15 +1,18 @@ import numpy as np import torch -from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM -from giant.model.network import Stage1Model, Stage2OneShot +from giant.constants import COND_DIM, SEC_SLOT_DIM, X_DIM +from giant.model.network import Stage1Model, Stage2OneShot, stage2_trunk_sec_dim from giant.validate import validate_marginals _PARTICLE_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} _MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} +_K_MAX = 5 -def _tiny_models(): +def _tiny_models(particle_type_cfg: dict | None = None): + """A fresh v0.3.0 pair: Stage1Model owns no n_sec_head (decision 1), so + n_sec always comes from Stage2OneShot.""" s1 = Stage1Model( pdg_vocab=3, mat_vocab=2, @@ -17,7 +20,13 @@ def _tiny_models(): material_cfg=_MATERIAL_CFG, hidden_dim=16, n_res_blocks=1, - n_sec_head_k_max=K_MAX, + ) + target = (particle_type_cfg or {}).get("target", "physical") + sec_dim = stage2_trunk_sec_dim( + particle_type_cfg or {"target": "physical"}, + "flow", + _K_MAX, + int(_PARTICLE_CFG["emb_dim"]), ) s2 = Stage2OneShot( pdg_vocab=3, @@ -28,23 +37,29 @@ def _tiny_models(): n_res_blocks=1, generator="flow", time_dim=16, + k_max=_K_MAX, + sec_dim=sec_dim, + particle_type_cfg=particle_type_cfg, ) + assert s2.particle_type_cfg.get("target", "physical") == target return s1.eval(), s2.eval() -def _zero_secondaries_loader(B=4, n_batches=2): - """A val_loader whose every batch has n_sec=0 (real side) — matches the - (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx) tuple shape - StreamingStepsDataset yields.""" +def _loader(B: int = 4, n_batches: int = 2, n_sec_value: int = 0, n_classes: int = 8): + """A val_loader matching StreamingStepsDataset's 7-tuple batch shape: + (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, sec_type_idx).""" batches = [] for _ in range(n_batches): cond_cont = torch.randn(B, COND_DIM) cond_cat = torch.zeros(B, 2, dtype=torch.long) x1 = torch.randn(B, X_DIM) - n_sec = torch.zeros(B, dtype=torch.long) - sec_cont = torch.zeros(B, K_MAX, SEC_SLOT_DIM) + n_sec = torch.full((B,), n_sec_value, dtype=torch.long) + sec_cont = torch.randn(B, _K_MAX, SEC_SLOT_DIM) proc_idx = torch.zeros(B, dtype=torch.long) - batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx)) + sec_type_idx = torch.randint(0, n_classes, (B, _K_MAX), dtype=torch.long) + batches.append( + (cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx) + ) return batches @@ -52,22 +67,56 @@ def test_validate_marginals_all_zero_secondaries_returns_nan_phys_kl(monkeypatch """If n_sec_pred collapses to 0 across the whole validated set (realistic during early/unstable training), phys_kl must degrade to NaN instead of crashing on the empty-array .min()/.max() reduction inside - _histogram_kl -- a regression the old species/bincount code this - replaced explicitly guarded against.""" + _histogram_kl.""" s1, s2 = _tiny_models() - loader = _zero_secondaries_loader() + loader = _loader(n_sec_value=0) - # Force the Stage-1 n_sec head's prediction to 0 for every sample too, so - # the generated side's valid-slot mask is also empty (real side is - # already all n_sec=0 by construction of the fake loader above). - def _fake_sample_flow(model, cond_cont, cond_cat, **kw): - B = cond_cont.size(0) - return torch.randn(B, X_DIM), torch.zeros(B, dtype=torch.long) + def _fake_resolve_n_sec( + stage1_model, sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred + ): + return torch.zeros(cond_cont.size(0), dtype=torch.long) - monkeypatch.setattr("giant.validate.sample_flow", _fake_sample_flow) + monkeypatch.setattr("giant.validate.resolve_n_sec", _fake_resolve_n_sec) - result = validate_marginals(s1, loader, sec_decoder=s2, n_batches=2) + result = validate_marginals(s1, loader, sec_decoder=s2, n_batches=2, steps=2) assert np.asarray(result["phys_real"]).shape == (0, 2) assert np.asarray(result["phys_generated"]).shape == (0, 2) assert np.isnan(np.asarray(result["phys_kl"])).all() + + +def test_validate_marginals_physical_target_shapes(): + s1, s2 = _tiny_models() + loader = _loader(n_sec_value=2) + + result = validate_marginals(s1, loader, sec_decoder=s2, n_batches=1, steps=2) + + assert np.asarray(result["real"]).shape == (4, X_DIM) + assert np.asarray(result["generated"]).shape == (4, X_DIM) + assert np.asarray(result["kl_divergence"]).shape == (X_DIM,) + assert "phys_real" in result and "phys_generated" in result and "phys_kl" in result + assert "type_class_real" not in result + + +def test_validate_marginals_onehot_type_class_marginal(): + particle_type_cfg = {"target": "onehot"} + s1, s2 = _tiny_models(particle_type_cfg) + loader = _loader(n_sec_value=2, n_classes=s2.type_dim) + + result = validate_marginals(s1, loader, sec_decoder=s2, n_batches=1, steps=2) + + assert "phys_real" not in result + # Real/generated valid-slot counts need not agree (real: ground-truth + # n_sec=2 always; generated: the untrained n_sec_head's own prediction). + assert np.asarray(result["type_class_real"]).ndim == 1 + assert np.asarray(result["type_class_gen"]).ndim == 1 + assert np.asarray(result["type_class_real"]).shape[0] > 0 + + +def test_validate_marginals_without_sec_decoder_returns_stage1_only(): + s1, _ = _tiny_models() + loader = _loader(n_sec_value=0) + + result = validate_marginals(s1, loader, n_batches=1, steps=2) + + assert set(result) == {"real", "generated", "kl_divergence"} diff --git a/tests/test_wgan.py b/tests/test_wgan.py index 07840b7..851527b 100644 --- a/tests/test_wgan.py +++ b/tests/test_wgan.py @@ -120,7 +120,7 @@ def test_sample_wgan_shape(): cond_cont, cond_cat = _cond(B) sample, n_sec = sample_wgan(model, cond_cont, cond_cat) assert sample.shape == (B, X_DIM) - assert n_sec.shape == (B,) + assert n_sec is not None and n_sec.shape == (B,) # --- Stage-2 generator/critic --- -- 2.39.5 From 8019a8056323a4d65889a487ac11f981fa9c633d Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 7 Aug 2026 17:03:20 +0200 Subject: [PATCH 12/30] Refactor train.py into giant/training/ around a metrics collector MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Every metric name used to exist in four places: the dict keys each StageTrainer returned, the hardcoded _metrics_fields() column list, the ~110-line metrics_row assembly in train(), and the tqdm/summary formatting. The two had to be kept in exact correspondence by hand or csv.DictWriter would raise. Each metric is now declared once, as a MetricSpec on the trainer that computes it. MetricsCollector derives the CSV header and W&B payload from those declarations and owns all accumulation, so train() no longer carries a running sum, and every isinstance(tr, WGANStageTrainer) branch is gone — replaced by four trainer hooks (batch_loss, summary, val_objective, supports_val_loss). giant/train.py (1875 lines) becomes giant/training/: trainers.py StageSpec + shared StageTrainer base + the two subclasses metrics.py MetricSpec, MetricsCollector stage2_inputs.py the pure AR/teacher-forcing tensor helpers, moved verbatim loop.py train() (225 lines, was ~514) + graceful shutdown checkpoint.py build/load, lifted out of train()'s closures The trainers shared ~15 identical constructor arguments and copy-pasted their cosine-warmup lambda, EMA setup, state_dict/load_state_dict, resume_lr and train_mode/eval_mode. StageSpec resolves one stage's config once (constructors go from 24 and 22 keyword arguments to (spec, model, device)), the base class holds the rest, and build_stage_trainers drops from ~100 lines to 15. Metric columns are renamed to a uniform stage/split/metric scheme (stage1/train/loss, stage2/train/d_loss, stage1/lr, stage1/router/entropy, val/loss, ...). Old metrics.csv files and W&B history are not comparable. The checkpoint format is unchanged. BEHAVIOR CHANGE — WGAN best-checkpoint selection. The old code meant to score a WGAN stage on its marginal KL, but the guard `{n: kl for n in wgan_names if n not in val_loss_per_stage}` could never fire: val_loss_per_stage was pre-seeded with 0.0 for every stage, so a WGAN stage contributed a flat 0.0 and the KL was written to metrics.csv without ever influencing best.pt. val_objective now returns it as intended. On the test harness's default flow+wgan config val_loss went from 2.182 (stage 1 only) to 15.137 (stage 1 + KL 12.954), and which epoch won changed. Runs before this commit picked their best checkpoint on the non-adversarial stages alone. Written up in docs/v0.3.0-followups.md. Verified: 699 tests pass; ruff, ruff format and ty clean. Baseline-vs- refactor metrics.csv compared across five configs (flow+wgan, AR+onehot, routed, both-flow, AR-flow) — every comparable value bit-identical except val/loss where the fix applies. Resume appends without a duplicate header and reproduces a HEAD worktree's per-epoch losses and LRs exactly across the resume boundary. A refactored last.pt loads through cli.py:_load_model_weights in both raw and ema modes. Co-Authored-By: Claude Opus 5 --- README.md | 9 +- docs/v0.3.0-design.md | 21 +- docs/v0.3.0-followups.md | 18 +- giant/cli.py | 4 +- giant/config.py | 2 +- giant/model/network.py | 2 +- giant/model/schedule.py | 2 +- giant/pipeline.py | 2 +- giant/sample.py | 2 +- giant/train.py | 1875 ------------------------------- giant/training/__init__.py | 30 + giant/training/checkpoint.py | 68 ++ giant/training/loop.py | 328 ++++++ giant/training/metrics.py | 405 +++++++ giant/training/stage2_inputs.py | 321 ++++++ giant/training/trainers.py | 987 ++++++++++++++++ scripts/hparam_scan.py | 4 +- tests/test_train.py | 80 +- 18 files changed, 2216 insertions(+), 1944 deletions(-) delete mode 100644 giant/train.py create mode 100644 giant/training/__init__.py create mode 100644 giant/training/checkpoint.py create mode 100644 giant/training/loop.py create mode 100644 giant/training/metrics.py create mode 100644 giant/training/stage2_inputs.py create mode 100644 giant/training/trainers.py diff --git a/README.md b/README.md index 02c395f..8365696 100644 --- a/README.md +++ b/README.md @@ -72,7 +72,12 @@ giant/ │ ├── materials.py # material name → (Z_eff, A_eff, density, X0, λ_int) │ ├── config.py # default hyperparameters, TOML config merging, device autodetect │ ├── pipeline.py # builds datasets/normalizers and kicks off a training run (with setup-stage caching) -│ ├── train.py # two-stage training loop, checkpointing, graceful shutdown, W&B logging +│ ├── training/ # two-stage training: loop, per-stage trainers, metrics, checkpointing +│ │ ├── loop.py # epoch loop, graceful shutdown, best-checkpoint selection +│ │ ├── trainers.py # StageSpec + flow/ddpm and WGAN-GP per-stage trainers +│ │ ├── stage2_inputs.py# ground-truth stage-2 targets + autoregressive/teacher-forcing inputs +│ │ ├── metrics.py # MetricsCollector: metrics.csv columns, W&B logging, progress/summary +│ │ └── checkpoint.py # checkpoint assembly/restore (format unchanged since v0.2) │ ├── sample.py # DDPM / DDIM / flow matching / WGAN samplers + secondary sampling │ ├── geometry.py # GeometryOracle: position → (material, layer_id, escaped) for rollout │ ├── rollout.py # autoregressive shower rollout driver @@ -125,7 +130,7 @@ dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl ``` -`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. +`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`. Metric names are `//` (e.g. `stage1/train/loss`, `stage2/train/d_loss`, `stage1/lr`), plus an unprefixed run-level tail (`val/loss`, `grad_norm`, `epoch_time_s`, ...); each is declared once on the `StageTrainer` that computes it (`giant/training/trainers.py`), so the CSV header and the W&B panel names are derived, never hand-maintained. 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. ## Validation and analysis diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md index 7a47aee..81f9698 100644 --- a/docs/v0.3.0-design.md +++ b/docs/v0.3.0-design.md @@ -322,7 +322,7 @@ stage1_context = "truth" | `lambda` | float | `1.0` | Weight of stage 2's loss in the total. Was `train.lambda_s2`. | | `k_max` | int | `15` | Maximum secondary slots. Under `"one_shot"` this is the fixed output width; under `"autoregressive"` it is a safety cap on the generation loop. Was the global constant `K_MAX` in `giant/constants.py` (max observed `n_sec` is 14 in the PbWO4 dataset, so 15 covers it with one spare). | | `context_dim` | int | `64` | Width of the projected stage-1 outcome fed into stage 2's conditioning. Was the hardcoded `stage1_proj_dim = 64`. | -| `stage1_context` | `"truth"` \| `"sampled"` | `"truth"` | What stage 2 conditions on during training. `"truth"`: the ground-truth stage-1 target vector, detached — v0.2 behaviour (`train.py:254` passes `x1_s1.detach()`), i.e. stage-level teacher forcing. `"sampled"`: stage 1's own sampled output, closing the train/inference gap at the cost of a sampling pass per batch and a moving target early in training. | +| `stage1_context` | `"truth"` \| `"sampled"` | `"truth"` | What stage 2 conditions on during training. `"truth"`: the ground-truth stage-1 target vector, detached — v0.2 behaviour (v0.2 `train.py:254` passes `x1_s1.detach()`), i.e. stage-level teacher forcing. `"sampled"`: stage 1's own sampled output, closing the train/inference gap at the cost of a sampling pass per batch and a moving target early in training. | #### `[stage2_model.n_sec]` @@ -748,7 +748,7 @@ configuration that plausibly meets the budget; flow AR is for quality comparison ## 7. Training loop -Decision 2 (full mixed per-stage objectives) makes `train.py` one trainer object +Decision 2 (full mixed per-stage objectives) makes `giant/training/` one trainer object per active stage: ```python @@ -765,8 +765,15 @@ FlowTrainer, DDPMTrainer, WGANTrainer(critic, n_critic, gp_weight) critic updates then a generator update. Independent optimizers, independent EMA. - Router auxiliary losses (`lambda_balance` / `lambda_proc` / `lambda_entropy`) become per-stage, summed over whichever stages are routed. Today's - `hasattr(model, "router")` check (`train.py:262`) generalizes cleanly. -- `metrics.csv` and W&B metric names gain a stage prefix. + `hasattr(model, "router")` check (v0.2 `train.py:262`) generalizes cleanly. +- `metrics.csv` and W&B metric names gain a stage prefix. **Implemented as** + `//` (`stage1/train/loss`, `stage2/train/d_loss`, + `stage1/lr`, `stage1/router/entropy`) plus an unprefixed run-level tail + (`val/loss`, `val/marginal_kl`, `grad_norm`, `gpu_mem_mb`, + `samples_per_sec`, `is_best`, `epoch_time_s`). Each name is declared once, + as a `MetricSpec` on the `StageTrainer` that computes it; the CSV header + and W&B payload are derived from those declarations + (`giant/training/metrics.py`). - With `stage1_model.active = false`, stage 2 still needs its stage-1 context: it comes from the ground-truth target already in the batch (`x1_s1`), which is exactly what v0.2 does anyway. **Stage-2-only training is therefore a cheap @@ -855,7 +862,7 @@ All in `giant/config.py`: | File | Why | |------|-----| | `giant/pipeline.py` | Builds `model_config`; now per-stage. Delete the wgan+router rejection at `:275`. Router `centers_init` seeding becomes per-stage. | -| `giant/train.py` | Per-stage trainers (§7). | +| `giant/training/` | Per-stage trainers (§7), metric collection, checkpointing, the epoch loop. | | `giant/cli.py` | Stage-prefixed flags for `train` and `new-run`; `build_models` now returns a dict (`:871`, `:1254`); `:1339` writes `model_config` into the rollout sidecar. | | `giant/sample.py` | Sampler picked per stage from `stage*_model.generator`; new AR sampling loop with KV cache under `history = "attention"`. | | `giant/rollout.py` | AR secondary generation; categorical class -> PDG decode; `other_policy` handling. | @@ -894,7 +901,7 @@ All in `giant/config.py`: later is a config addition, not a break. - **`stage2_model.generator = "ddpm"`.** The value is **accepted by the schema** (§3.3 lists `"flow" | "ddpm" | "wgan"` with no caveat) but - `FlowDDPMStageTrainer.__init__` (`giant/train.py`) raises + `FlowDDPMStageTrainer.__init__` (`giant/training/trainers.py`) raises `NotImplementedError` for stage 2 — only `"flow"` and `"wgan"` have a stage-2 secondary-decoder loss implemented. `stage1_model.generator = "ddpm"` is unaffected; this restriction is stage-2-only. Landing stage-2 @@ -968,7 +975,7 @@ or a non-adversarial CE head, both of which already exist as config options. 2. **`network.py`** — the §5 decomposition, with `Stage2OneShot` reproducing v0.2 exactly. Gate on the §4.3 migration test: load a v0.2 checkpoint through the shim and diff outputs against v0.2 code. -3. **`train.py`** — per-stage trainers; `active = false` paths. At this point +3. **`giant/training/`** — per-stage trainers; `active = false` paths. At this point Stage-2-only training works and the meeting's step 2 (one-shot WGAN baseline, trained standalone) is runnable. 4. **Type map** — `loader.py` + `setup_cache.py` + checkpoint persistence + diff --git a/docs/v0.3.0-followups.md b/docs/v0.3.0-followups.md index 48ea25b..9a9caef 100644 --- a/docs/v0.3.0-followups.md +++ b/docs/v0.3.0-followups.md @@ -30,8 +30,24 @@ turned out fine: - `analysis/render.py`/`analysis/router_gating.py` correctly branch old-flat vs new-nested `model_config["router"]` location — doc flagged this as a likely stale spot (§10) but it's actually fine. -- `train.py`'s per-stage trainers, mixed flow+wgan runs, WGAN critic cadence, +- `giant/training/`'s per-stage trainers, mixed flow+wgan runs, WGAN critic cadence, per-stage router auxiliary losses, stage-prefixed metrics, stage-2-only training via ground-truth `x1_s1` (§7). + +## Behavior change: WGAN best-checkpoint selection + +The `giant/training/` split fixed a dead guard in WGAN validation scoring. A +WGAN stage was *meant* to contribute its marginal KL to the `val_loss` that +drives `best.pt`, but the guard `if n not in val_loss_per_stage` could never +fire (every stage was pre-seeded to `0.0`), so the stage contributed a flat +`0.0` and the KL was written to `metrics.csv` without ever being used. +`WGANStageTrainer.val_objective` now returns the KL as intended. + +**Consequence:** any checkpoint selected before this commit under a config +with a WGAN stage — including the v0.3.0 default (`stage2_model.generator = +"wgan"`) — picked its best epoch on the non-adversarial stages alone. Measured +on the test harness's default flow+wgan config, `val_loss` went from `2.182` +(stage 1 only) to `15.137` (stage 1 + KL `12.954`), and which epoch won +changed. Do not compare `val/loss` or `best.pt` choice across this commit. - `pipeline.py`'s deleted wgan+router rejection, per-stage `centers_init` seeding, removed stale expert-size warning (§9, §10). diff --git a/giant/cli.py b/giant/cli.py index 81ac407..dcd2590 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -316,7 +316,7 @@ def _load_model_weights( ) -> None: """Load either the raw or EMA state dicts from a training checkpoint. - EMA weights (giant.train's shadow copy, see --ema-decay) only exist in + EMA weights (giant.training's shadow copy, see --ema-decay) only exist in checkpoints written after that feature landed, so `ema` fails loudly rather than silently falling back to raw weights a caller didn't ask for. """ @@ -877,7 +877,7 @@ def train( # Name only encodes what's non-default (see default_out_dir_name), so # two runs with identical hyperparams in the same to-the-minute # timestamp would otherwise collide on this name — which also - # doubles as the W&B run id (giant.train) — hence the suffix loop in + # doubles as the W&B run id (giant.training) — hence the suffix loop in # resolve_default_out_dir. out_dir = gconfig.resolve_default_out_dir(cfg) diff --git a/giant/config.py b/giant/config.py index 28ed354..95fc1e6 100644 --- a/giant/config.py +++ b/giant/config.py @@ -894,7 +894,7 @@ def default_out_dir_name(cfg: dict, now: datetime | None = None) -> str: Beyond `_OUT_DIR_NAME_MAX_FIELDS` non-default fields, the remainder collapse into a short deterministic hash suffix rather than growing the name unboundedly. This name doubles as the run's W&B id (see - giant.train), which is the reason a timestamp is always included. + giant.training), which is the reason a timestamp is always included. """ now = now or datetime.now() tokens = [] diff --git a/giant/model/network.py b/giant/model/network.py index f7161aa..ce91890 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -886,7 +886,7 @@ class AttentionHistory(HistoryEncoder): `Stage2Autoregressive._token_cond` use: `feat[:, i]` is token `i - 1`'s own `(energy_fraction, direction, type_representation)`, with a learned start vector substituted at `has_prev == False` positions (only slot 0 in - practice — see `giant.train._ar_has_prev`). Causal masking then makes + practice — see `giant.training.stage2_inputs._ar_has_prev`). Causal masking then makes position `i`'s output a function of `feat[:, 1:i+1]` — i.e. tokens `0..i-1` — exactly the prefix available when predicting token `i`. diff --git a/giant/model/schedule.py b/giant/model/schedule.py index 25aff5e..27d8282 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -159,7 +159,7 @@ def flow_matching_loss_secondary_ar( Under teacher forcing (docs/v0.3.0-design.md §6.2 point 3) this is still a single parallel pass over all K_MAX tokens — `x1`/`history_feat`/etc. are already built from ground truth for every slot by the caller - (`giant.train._assemble_stage2_ar_inputs`/`_assemble_stage2_ar_target`). + (`giant.training.stage2_inputs._assemble_stage2_ar_inputs`/`_assemble_stage2_ar_target`). x1: (B, K_MAX, CONT_SLOT_DIM + type_dim) — per-token flattened target (stick_logit, dir, then a `type_dim`-wide type slice) diff --git a/giant/pipeline.py b/giant/pipeline.py index a10eea8..c427a1b 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -32,7 +32,7 @@ from giant.data.transforms import ( ) from giant.data.dataset import make_event_split, StreamingStepsDataset from giant.model.network import build_models, build_critics -from giant.train import train as run_training +from giant.training import train as run_training @dataclass diff --git a/giant/sample.py b/giant/sample.py index 2f8df2f..e5fb65f 100644 --- a/giant/sample.py +++ b/giant/sample.py @@ -245,7 +245,7 @@ def sample_secondaries_ar( one token at a time, in descending-energy slot order, `k_max` sequential calls. Unlike training (teacher forcing, §6.2 point 3 — a single parallel pass over ground-truth tokens, see - `giant.train._assemble_stage2_ar_inputs`), there is no ground truth at + `giant.training.stage2_inputs._assemble_stage2_ar_inputs`), there is no ground truth at inference: each token's conditioning is built free-running, from the PREVIOUS TOKEN'S OWN just-generated output — the train/inference gap §6.2 point 4 explicitly flags as the cost of markov history's diff --git a/giant/train.py b/giant/train.py deleted file mode 100644 index 774638c..0000000 --- a/giant/train.py +++ /dev/null @@ -1,1875 +0,0 @@ -import copy -import csv -import math -import os -import signal -import time -from pathlib import Path -from types import FrameType -from typing import Callable - -import numpy as np -import torch -import torch.nn.functional as F -import torch.optim as optim -from torch.utils.data import DataLoader -from tqdm import tqdm - -from giant.constants import CONT_SLOT_DIM, PARTICLE_PHYS_DIM -from giant.data.loader import TopNMap -from giant.data.setup_cache import topnmap_to_json -from giant.model.network import Router, stage2_type_dim -from giant.model.schedule import ( - CosineSchedule, - flow_matching_loss, - flow_matching_loss_secondary, - flow_matching_loss_secondary_ar, -) -from giant.model.wgan import gradient_penalty, generator_loss -from giant.sample import sample_secondaries_ar -from giant.validate import validate_marginals - -_CATCHABLE_SIGNALS = (signal.SIGINT, signal.SIGTERM) - - -class _GracefulShutdown: - """Turns SIGINT/SIGTERM into a flag check instead of an immediate crash. - - A second signal while already shutting down restores the default - handler and re-sends the signal, so an unresponsive run can still be - force-killed. - """ - - def __init__(self) -> None: - self.requested = False - self._previous: dict[ - int, - Callable[[int, FrameType | None], object] | signal.Handlers | int | None, - ] = {} - - def __enter__(self) -> "_GracefulShutdown": - for sig in _CATCHABLE_SIGNALS: - self._previous[sig] = signal.getsignal(sig) - signal.signal(sig, self._handle) - return self - - def __exit__(self, *exc_info) -> None: - for sig, handler in self._previous.items(): - signal.signal(sig, handler) - - def _handle(self, signum: int, frame) -> None: - if self.requested: - signal.signal(signum, self._previous[signum]) - os.kill(os.getpid(), signum) - return - self.requested = True - print( - f"\nreceived {signal.Signals(signum).name} — finishing the current " - "batch, then saving a checkpoint and exiting (send again to force-quit)" - ) - - -@torch.no_grad() -def _update_ema( - ema_model: torch.nn.Module, model: torch.nn.Module, decay: float -) -> None: - for ema_p, p in zip(ema_model.parameters(), model.parameters()): - ema_p.mul_(decay).add_(p, alpha=1 - decay) - - -def _gumbel_tau(step: int, total_steps: int, tau_start: float, tau_end: float) -> float: - """Linear anneal of the straight-through Gumbel-softmax temperature. - - Deterministic in `step`/`total_steps` alone (no extra state), so it - recomputes correctly on `--resume` from a checkpoint's saved `global_step` - without needing to persist anything new (see - giant.model.network.Router.combine_weights). - """ - progress = min(step / max(total_steps, 1), 1.0) - return tau_start + (tau_end - tau_start) * progress - - -def _stage_router(model: torch.nn.Module) -> Router | None: - """A stage model's Router, if its trunk is routed — else None. - - Post-step-2 refactor the router lives at `model.trunk.router` - (`giant.model.network.RoutedTrunk`), not `model.router` directly. - """ - trunk = getattr(model, "trunk", None) - return getattr(trunk, "router", None) - - -def _batch_to_device(batch: tuple, device: torch.device) -> tuple: - return tuple(t.to(device) for t in batch) - - -def _type_repr( - sec_type_idx: torch.Tensor, - sec_cont: torch.Tensor, - particle_type_cfg: dict, - cond_enc: torch.nn.Module, - emb_dim: int, -) -> torch.Tensor: - """(B, K_MAX, type_dim) ground-truth type representation, generator- - independent (unlike `_assemble_stage2_ar_target`'s training *target*, - which varies by generator/objective — see its docstring): `"physical"` -> - `(log_mass, charge)`; `"onehot"` -> one-hot of the true class; - `"embedding"` -> the conditioning's own detached embedding-table row. - - Used both to build `_assemble_stage2_ar_target`'s wgan+onehot/embedding - branch and as the AR history features' previous-secondary identity — the - latter must always reflect the true physical secondary that came before, - regardless of what the *current* token's own training objective is. - """ - target = particle_type_cfg.get("target", "physical") - if target == "physical": - return sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM] - if target == "onehot": - return F.one_hot(sec_type_idx, num_classes=emb_dim).float() - return cond_enc.pdg_emb(sec_type_idx).detach() - - -def _assemble_stage2_ar_target( - sec_cont: torch.Tensor, - sec_type_idx: torch.Tensor, - particle_type_cfg: dict, - generator: str, - cond_enc: torch.nn.Module, - emb_dim: int, -) -> torch.Tensor: - """(B, K_MAX, token_dim) ground-truth per-token target — the unflattened - analogue of `_assemble_stage2_real` (defined below in terms of this), - matching whatever width `Stage2Autoregressive`'s (or `Stage2OneShot`'s) - own trunk produces for this (target, generator) combination - (`giant.model.network.stage2_trunk_sec_dim`; docs/v0.3.0-design.md - decision 2/3): - - - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, - dir, log_mass, charge) as-is. - - `target` in `("onehot", "embedding")` + `generator in ("flow", "ddpm")`: - just the continuous stick/dir slots — the type slice isn't part of - this tensor at all (`type_head` handles it separately). - - `target` in `("onehot", "embedding")` + `generator == "wgan"`: stick/dir - slots concatenated with the per-slot type representation (a one-hot of - the true class, relaxed on the *generated* side only, by the caller; - or the conditioning's own detached embedding-table row). - """ - target = particle_type_cfg.get("target", "physical") - if target == "physical": - return sec_cont - cont = sec_cont[..., :CONT_SLOT_DIM] - if generator != "wgan": - return cont - type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) - return torch.cat([cont, type_repr], dim=-1) - - -def _assemble_stage2_real( - sec_cont: torch.Tensor, - sec_type_idx: torch.Tensor, - particle_type_cfg: dict, - generator: str, - cond_enc: torch.nn.Module, - emb_dim: int, -) -> torch.Tensor: - """Ground-truth flattened stage-2 vector for `Stage2OneShot` — the - flattened form of `_assemble_stage2_ar_target`, which - `Stage2Autoregressive`'s per-token target also uses; the two must stay in - lockstep. See `_assemble_stage2_ar_target`'s docstring for the - (target, generator) width rules.""" - return _assemble_stage2_ar_target( - sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim - ).flatten(1) - - -def _stick_fraction(sec_cont: torch.Tensor) -> torch.Tensor: - """(B, K_MAX) — sigmoid of each slot's own stick-breaking logit - (`sec_cont[...,0]`); scale-free (see `giant.data.transforms. - encode_secondaries`), so this needs no absolute `e_sec`.""" - return torch.sigmoid(sec_cont[..., 0]) - - -def _remaining_energy_fraction(fraction: torch.Tensor) -> torch.Tensor: - """(B, K_MAX) — fraction of the original e_sec budget unclaimed entering - slot i: `1.0` at `i=0`, `prod_{j=1` - (docs/v0.3.0-design.md §6.3 — "no re-derivation needed": the existing - stick-breaking encoding is already scale-free, so this is derivable from - the batch's ground-truth stick logits alone, no `e_sec` required).""" - cumprod = torch.cumprod(1.0 - fraction, dim=1) - return torch.cat([torch.ones_like(cumprod[:, :1]), cumprod[:, :-1]], dim=1) - - -def _shift_prev(x: torch.Tensor) -> torch.Tensor: - """`(B, K, ...)` -> same shape, slot i holds slot i-1's value; slot 0 gets - an arbitrary zero placeholder (never read as-is — see `_ar_has_prev`; - `MarkovHistory` substitutes its own learned start vector there instead).""" - return torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) - - -def _ar_has_prev(k_max: int, device: torch.device) -> torch.Tensor: - """`(1, K_MAX)` bool: True for slot index `>= 1`. Correct without - `n_sec`: `sec_mask` is a prefix mask, so any *valid* token at `k>=1` - always has a valid predecessor at `k-1`; the only wrong cases are tokens - that are themselves padding, already masked out of every loss.""" - return (torch.arange(k_max, device=device) >= 1).unsqueeze(0) - - -def _assemble_stage2_ar_inputs( - sec_cont: torch.Tensor, - sec_type_idx: torch.Tensor, - particle_type_cfg: dict, - cond_enc: torch.nn.Module, - emb_dim: int, -) -> dict[str, torch.Tensor]: - """Ground-truth per-token AR conditioning tensors — all `(B, K_MAX, ...)` - or `(B, K_MAX)`, built in one vectorized pass (teacher forcing means - every token's input is ground truth, docs/v0.3.0-design.md §6.2 point 3). - Keys match `Stage2Autoregressive.forward`'s trailing kwargs.""" - device = sec_cont.device - B, K = sec_cont.shape[0], sec_cont.shape[1] - fraction = _stick_fraction(sec_cont) - type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) - history_feat = torch.cat( - [ - _shift_prev(fraction).unsqueeze(-1), - _shift_prev(sec_cont[..., 1:CONT_SLOT_DIM]), - _shift_prev(type_repr), - ], - dim=-1, - ) - slot_idx = (torch.arange(K, device=device).float() / max(K - 1, 1)).unsqueeze(0) - return { - "history_feat": history_feat, - "has_prev": _ar_has_prev(K, device).expand(B, -1), - "remaining_frac": _remaining_energy_fraction(fraction), - "slot_idx": slot_idx.expand(B, -1), - } - - -def _stage2_tf_prob( - mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int -) -> float: - """P(condition slot k+1 on the TRUE token k rather than the model's own - prediction), for the current epoch (docs/v0.3.0-design.md §3.3 - `stage2_model.autoregressive.teacher_forcing`). `"always"`/`"never"` are - the two degenerate constants; `"scheduled"` linearly interpolates - `p_start` (epoch 0) to `p_end` (the final epoch) — standard scheduled - sampling (Bengio et al. 2015).""" - if mode == "always": - return 1.0 - if mode == "never": - return 0.0 - frac = epoch / max(total_epochs - 1, 1) - frac = min(max(frac, 0.0), 1.0) - return p_start + (p_end - p_start) * frac - - -def _history_repr_from_ar_sample( - sec_cont_pred: torch.Tensor, - sec_type_pred: torch.Tensor, - particle_type_cfg: dict, -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """`(fraction, direction, type_repr)` — the same triple `_type_repr` / - `_stick_fraction` derive from ground truth, but from a free-running - `sample_secondaries_ar` self-sample instead, so the two can be mixed - slot-by-slot under scheduled sampling (`_assemble_stage2_ar_inputs_scheduled`). - `target="onehot"` collapses the raw per-slot type logits to a hard - one-hot of `argmax` — `sample_secondaries_ar`'s own history convention - (see its docstring), matching what `MarkovHistory`/`AttentionHistory` - were trained on; the other two targets are already the right - representation.""" - fraction = torch.sigmoid(sec_cont_pred[..., 0]) - direction = sec_cont_pred[..., 1:4] - if particle_type_cfg.get("target", "physical") == "onehot": - type_dim = sec_type_pred.size(-1) - type_repr = F.one_hot(sec_type_pred.argmax(-1), num_classes=type_dim).float() - else: - type_repr = sec_type_pred - return fraction, direction, type_repr - - -def _assemble_stage2_ar_inputs_scheduled( - model: torch.nn.Module, - cond_cont: torch.Tensor, - cond_cat: torch.Tensor, - stage1_ctx: torch.Tensor, - sec_cont: torch.Tensor, - sec_type_idx: torch.Tensor, - n_sec: torch.Tensor, - particle_type_cfg: dict, - cond_enc: torch.nn.Module, - emb_dim: int, - p_tf: float, - sample_steps: int, -) -> dict[str, torch.Tensor]: - """Scheduled-sampling counterpart of `_assemble_stage2_ar_inputs` - (docs/v0.3.0-design.md §3.3 `teacher_forcing` = "scheduled"/"never"): - each slot's history is the TRUE previous token with probability `p_tf` - (an independent per-example, per-slot Bernoulli draw) and the model's own - free-running prediction otherwise — closing the train/inference gap that - `teacher_forcing="always"` (ground truth throughout training) never sees. - `p_tf >= 1.0` degenerates exactly to `_assemble_stage2_ar_inputs` (and - skips self-sampling entirely), so callers can call this unconditionally. - - The free-running estimate is a REAL autoregressive self-sample — - `giant.sample.sample_secondaries_ar` under `torch.no_grad()` — not a - cheap one-step proxy, so building it costs the same `k_max` (`* steps` - for flow) sequential forwards `sample.py` pays at inference, EVERY batch - this is called on (§6.4's cost note, paid at train time too whenever - teacher_forcing != "always"). Fully detached: gradient only ever flows - through the "real" target path each stage trainer already uses - (`_assemble_stage2_ar_target`), never through this self-sample. - """ - device = sec_cont.device - B, K = sec_cont.shape[0], sec_cont.shape[1] - if p_tf >= 1.0: - return _assemble_stage2_ar_inputs( - sec_cont, sec_type_idx, particle_type_cfg, cond_enc, emb_dim - ) - - was_training = model.training - sec_cont_pred, sec_type_pred, _ = sample_secondaries_ar( - model, cond_cont, cond_cat, stage1_ctx, n_sec, steps=sample_steps - ) - if was_training: - model.train() - - fraction_gt = _stick_fraction(sec_cont) - dir_gt = sec_cont[..., 1:4] - type_repr_gt = _type_repr( - sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim - ) - fraction_pred, dir_pred, type_repr_pred = _history_repr_from_ar_sample( - sec_cont_pred, sec_type_pred, particle_type_cfg - ) - - use_gt = torch.rand(B, K, device=device) < p_tf - fraction = torch.where(use_gt, fraction_gt, fraction_pred) - direction = torch.where(use_gt.unsqueeze(-1), dir_gt, dir_pred) - type_repr = torch.where(use_gt.unsqueeze(-1), type_repr_gt, type_repr_pred) - - own_feat = torch.cat([fraction.unsqueeze(-1), direction, type_repr], dim=-1) - return { - "history_feat": _shift_prev(own_feat), - "has_prev": _ar_has_prev(K, device).expand(B, -1), - "remaining_frac": _remaining_energy_fraction(fraction), - "slot_idx": (torch.arange(K, device=device).float() / max(K - 1, 1)) - .unsqueeze(0) - .expand(B, -1), - } - - -def _relax_onehot_type_slice( - x_flat: torch.Tensor, - k_max: int, - cont_dim: int, - type_dim: int, - tau: float, - grad_probe: dict[str, float] | None = None, -) -> torch.Tensor: - """Straight-through Gumbel-softmax relaxation of the per-slot type slice - inside a flattened `(B, k_max * (cont_dim + type_dim))` WGAN generator - output — decision 5 (docs/v0.3.0-design.md §2.1): the forward pass is a - hard one-hot (matching what the critic sees from real data), the - backward pass flows smooth gradient. Continuous slots (stick/dir, and - the type slice itself under `target = "embedding"`, which never calls - this) pass through unchanged. - - `grad_probe`, if given, gets `["cont"]`/`["type"]` populated with the L2 - norm of the gradient reaching this split point during the next - `.backward()` call that touches it — a backward hook, not a second - backward pass. This is the §11.4 differentiability validation-obligation - instrumentation (docs/v0.3.0-design.md): the trunk-gradient contribution - from the type slice vs. the continuous slices, for - `particle_type.target="onehot"` + `generator="wgan"`. Only ever populated - on a `did_g_step` batch — the critic step backprops through - `fake.detach()`, which never reaches these hooks — so it stays empty - (callers default to `0.0`) otherwise.""" - B = x_flat.size(0) - x = x_flat.view(B, k_max, cont_dim + type_dim) - cont, type_logits = x[..., :cont_dim], x[..., cont_dim:] - if grad_probe is not None: - cont.register_hook(lambda g: grad_probe.__setitem__("cont", g.norm().item())) - type_logits.register_hook( - lambda g: grad_probe.__setitem__("type", g.norm().item()) - ) - type_soft = F.gumbel_softmax(type_logits, tau=tau, hard=True, dim=-1) - return torch.cat([cont, type_soft], dim=-1).reshape(B, -1) - - -class StageTrainer: - """One active stage's optimizer(s), EMA, and per-batch step. - - Reads only the shared batch tuple `(cond_cont, cond_cat, x1_s1, n_sec, - sec_cont, proc_idx)` — stage 2 always conditions on the ground-truth - `x1_s1` (`stage2_model.stage1_context = "truth"`, stage-level teacher - forcing; `"sampled"` is not implemented — see docs/v0.3.0-design.md §3.3), - so stage trainers never need each other's output at train time. This - means "stage-2-only training is a cheap ablation, not new plumbing" - (design doc §7) falls out for free: a trainer only exists for active - stages, and inactive stages are simply never constructed. - - Grad-norm clipping is per-stage here — v0.2's single shared optimizer - clipped both stages' gradients jointly; splitting per stage is a small, - disclosed behavior change. It doesn't affect Adam's per-parameter update - math itself (no cross-parameter coupling), only the clip threshold's - scope. - """ - - name: str - is_stage2: bool - model: torch.nn.Module - ema_model: torch.nn.Module | None - router: Router | None - optimizer: optim.Optimizer - - def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: - raise NotImplementedError - - def val_loss(self, batch: tuple, device: torch.device) -> dict: - raise NotImplementedError - - def train_mode(self) -> None: - raise NotImplementedError - - def eval_mode(self) -> None: - raise NotImplementedError - - def sampling_model(self) -> torch.nn.Module: - return self.ema_model if self.ema_model is not None else self.model - - def state_dict(self) -> dict: - raise NotImplementedError - - def load_state_dict(self, sd: dict) -> None: - raise NotImplementedError - - def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: - raise NotImplementedError - - -class FlowDDPMStageTrainer(StageTrainer): - """flow or ddpm generator for a single stage.""" - - def __init__( - self, - name: str, - model: torch.nn.Module, - is_stage2: bool, - generator: str, - lambda_weight: float, - n_sec_lambda: float, - lambda_balance: float, - lambda_proc: float, - lambda_entropy: float, - gumbel_tau_start: float, - gumbel_tau_end: float, - lr: float, - weight_decay: float, - ema_decay: float, - warmup_epochs: int, - epochs: int, - steps_per_epoch: int, - ddpm_n_steps: int, - device: torch.device, - particle_type_cfg: dict | None = None, - particle_type_emb_dim: int = 16, - decoder: str = "one_shot", - teacher_forcing: str = "always", - tf_p_start: float = 1.0, - tf_p_end: float = 1.0, - ar_sample_steps: int = 10, - ) -> None: - if is_stage2 and generator not in ("flow",): - raise NotImplementedError( - f"stage2_model.generator={generator!r} is accepted by the " - "schema but not implemented in v0.3.0 for stage 2 (only " - "'flow' and 'wgan' have a stage-2 secondary-decoder loss — " - "see docs/v0.3.0-design.md §11.2)" - ) - self.name = name - self.is_stage2 = is_stage2 - self.generator = generator - self.decoder = decoder - self.teacher_forcing = teacher_forcing - self.tf_p_start = tf_p_start - self.tf_p_end = tf_p_end - self.ar_sample_steps = ar_sample_steps - self.total_epochs = epochs - self.steps_per_epoch = max(steps_per_epoch, 1) - self.device = device - self.model = model.to(device) - self.lambda_weight = lambda_weight - self.n_sec_lambda = n_sec_lambda - self.lambda_balance = lambda_balance - self.lambda_proc = lambda_proc - self.lambda_entropy = lambda_entropy - self.gumbel_tau_start = gumbel_tau_start - self.gumbel_tau_end = gumbel_tau_end - self.ema_decay = ema_decay - self.router = _stage_router(self.model) - - self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) - self.particle_type_emb_dim = particle_type_emb_dim - self.particle_type_lambda = self.particle_type_cfg.get("lambda", 1.0) - # Width of the type slice actually folded into x1_s2 by - # _assemble_stage2_real, under this trainer's generator (flow/ddpm - # only — see the NotImplementedError above): "physical" keeps it - # folded in (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/ - # "embedding" pull it out into model.type_head instead (0 here). - self._flow_type_dim = ( - None - if self.particle_type_cfg.get("target", "physical") == "physical" - else 0 - ) - - self.params = list(self.model.parameters()) - self.optimizer = optim.AdamW(self.params, lr=lr, weight_decay=weight_decay) - warmup_steps = warmup_epochs * steps_per_epoch - total_steps = max(epochs * steps_per_epoch, 1) - - def _lr_lambda(step: int) -> float: - if warmup_steps > 0 and step < warmup_steps: - return (step + 1) / warmup_steps - t = step - warmup_steps - T = max(total_steps - warmup_steps, 1) - return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) - - self._lr_lambda = _lr_lambda - self.lr_sched = optim.lr_scheduler.LambdaLR(self.optimizer, _lr_lambda) - self.total_steps = total_steps - self.ddpm_schedule = ( - CosineSchedule(T=ddpm_n_steps).to(device) if generator == "ddpm" else None - ) - - self.ema_model: torch.nn.Module | None = None - if ema_decay > 0: - self.ema_model = copy.deepcopy(self.model).eval() - for p in self.ema_model.parameters(): - p.requires_grad_(False) - - def _predict_n_sec(self, cond_cont, cond_cat, stage1_ctx): - if self.model.n_sec_head is None: - return None - if self.is_stage2: - return self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) - return self.model.predict_n_sec(cond_cont, cond_cat) - - def _generator_loss( - self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None - ): - if not self.is_stage2: - if self.generator == "flow": - return flow_matching_loss(self.model, x1_s1, cond_cont, cond_cat) - assert self.ddpm_schedule is not None - return self.ddpm_schedule.loss(self.model, x1_s1, cond_cont, cond_cat) - if self.decoder == "autoregressive": - assert ar_inputs is not None - return flow_matching_loss_secondary_ar( - self.model, - x1_s2, - cond_cont, - cond_cat, - stage1_ctx, - ar_inputs["history_feat"], - ar_inputs["has_prev"], - ar_inputs["remaining_frac"], - ar_inputs["slot_idx"], - sec_mask, - type_dim=self._flow_type_dim, - ) - return flow_matching_loss_secondary( - self.model, - x1_s2, - cond_cont, - cond_cat, - stage1_ctx, - sec_mask, - type_dim=self._flow_type_dim, - ) - - def _type_loss( - self, - cond_cont, - cond_cat, - stage1_ctx, - sec_type_idx, - sec_mask, - device, - ar_inputs=None, - ): - """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for the - stage-2 model's `type_head` — the non-adversarial counterpart to - WGANStageTrainer's ST-Gumbel-into-the-critic path (decision 2/5). - Zero when this stage has no `type_head` (stage 1, or - `particle_type.target = "physical"`).""" - l_type = torch.zeros((), device=device) - type_acc = torch.zeros((), device=device) - type_head = getattr(self.model, "type_head", None) - if not self.is_stage2 or type_head is None: - return l_type, type_acc - if self.decoder == "autoregressive": - assert ar_inputs is not None - type_out = self.model.predict_type( - cond_cont, - cond_cat, - stage1_ctx, - ar_inputs["history_feat"], - ar_inputs["has_prev"], - ar_inputs["remaining_frac"], - ar_inputs["slot_idx"], - ) - else: - type_out = self.model.predict_type(cond_cont, cond_cat, stage1_ctx) - mask = sec_mask.float() - denom = mask.sum().clamp(min=1) - if self.particle_type_cfg.get("target") == "onehot": - ce = F.cross_entropy( - type_out.transpose(1, 2), sec_type_idx, reduction="none" - ) - l_type = (ce * mask).sum() / denom - type_acc = ( - (type_out.argmax(-1) == sec_type_idx).float() * mask - ).sum() / denom - else: # "embedding" - target_vec = self.model.cond_enc.pdg_emb(sec_type_idx).detach() - se = ((type_out - target_vec) ** 2).mean(-1) - l_type = (se * mask).sum() / denom - return l_type, type_acc - - def _compute( - self, batch: tuple, device: torch.device, epoch: int | None = None - ) -> dict: - """`epoch=None` (the `val_loss` path) always uses full teacher - forcing (`p_tf=1.0`) regardless of `self.teacher_forcing` — validation - should stay a stable, non-stochastic ground-truth comparison; only - the training `step` path schedules `p_tf` by epoch.""" - ( - cond_cont, - cond_cat, - x1_s1, - n_sec, - sec_cont, - proc_idx, - sec_type_idx, - ) = _batch_to_device(batch, device) - sec_mask = torch.arange(sec_cont.size(1), device=device).unsqueeze( - 0 - ) < n_sec.unsqueeze(1) - stage1_ctx = x1_s1.detach() - - x1_s2 = None - ar_inputs = None - if self.is_stage2 and self.decoder == "autoregressive": - p_tf = ( - 1.0 - if epoch is None - else _stage2_tf_prob( - self.teacher_forcing, - self.tf_p_start, - self.tf_p_end, - epoch, - self.total_epochs, - ) - ) - ar_inputs = _assemble_stage2_ar_inputs_scheduled( - self.model, - cond_cont, - cond_cat, - stage1_ctx, - sec_cont, - sec_type_idx, - n_sec, - self.particle_type_cfg, - self.model.cond_enc, - self.particle_type_emb_dim, - p_tf, - self.ar_sample_steps, - ) - x1_s2 = _assemble_stage2_ar_target( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - self.generator, - self.model.cond_enc, - self.particle_type_emb_dim, - ) - elif self.is_stage2: - x1_s2 = _assemble_stage2_real( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - self.generator, - self.model.cond_enc, - self.particle_type_emb_dim, - ) - - l_gen = self._generator_loss( - cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs - ) - n_sec_logits = self._predict_n_sec(cond_cont, cond_cat, stage1_ctx) - l_nsec = torch.zeros((), device=device) - nsec_acc = torch.zeros((), device=device) - if n_sec_logits is not None: - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() - - l_type, type_acc = self._type_loss( - cond_cont, - cond_cat, - stage1_ctx, - sec_type_idx, - sec_mask, - device, - ar_inputs=ar_inputs, - ) - - l_balance = l_proc = l_entropy = torch.zeros((), device=device) - if self.router is not None: - l_balance = self.router.balance_loss(cond_cont, cond_cat) - l_proc = self.router.classify_loss(cond_cont, cond_cat, proc_idx) - l_entropy = self.router.entropy_loss(cond_cont, cond_cat) - - total = ( - self.lambda_weight * l_gen - + self.n_sec_lambda * l_nsec - + self.particle_type_lambda * l_type - ) - if self.lambda_balance > 0: - total = total + self.lambda_balance * l_balance - if self.lambda_proc > 0: - total = total + self.lambda_proc * l_proc - if self.lambda_entropy > 0: - total = total + self.lambda_entropy * l_entropy - - return { - "total": total, - "loss_gen": l_gen, - "loss_nsec": l_nsec, - "loss_type": l_type, - "type_acc": type_acc, - "loss_balance": l_balance, - "loss_proc": l_proc, - "loss_entropy": l_entropy, - "nsec_acc": nsec_acc, - "batch_size": cond_cont.size(0), - } - - def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: - if self.router is not None: - self.router.gumbel_tau = _gumbel_tau( - global_step, - self.total_steps, - self.gumbel_tau_start, - self.gumbel_tau_end, - ) - epoch = global_step // self.steps_per_epoch - out = self._compute(batch, device, epoch=epoch) - self.optimizer.zero_grad() - out["total"].backward() - grad_norm = torch.nn.utils.clip_grad_norm_(self.params, 1.0) - self.optimizer.step() - self.lr_sched.step() - if self.ema_model is not None: - _update_ema(self.ema_model, self.model, self.ema_decay) - return { - "loss": out["total"].item(), - "loss_gen": out["loss_gen"].item(), - "loss_nsec": out["loss_nsec"].item(), - "loss_type": out["loss_type"].item(), - "type_acc": out["type_acc"].item(), - "loss_balance": out["loss_balance"].item(), - "loss_proc": out["loss_proc"].item(), - "loss_entropy": out["loss_entropy"].item(), - "nsec_acc": out["nsec_acc"].item(), - "grad_norm": grad_norm.item(), - "lr": self.optimizer.param_groups[0]["lr"], - "batch_size": out["batch_size"], - } - - @torch.no_grad() - def val_loss(self, batch: tuple, device: torch.device) -> dict: - out = self._compute(batch, device) - return { - "loss": out["total"].item(), - "loss_gen": out["loss_gen"].item(), - "loss_nsec": out["loss_nsec"].item(), - "loss_type": out["loss_type"].item(), - "type_acc": out["type_acc"].item(), - "loss_balance": out["loss_balance"].item(), - "loss_proc": out["loss_proc"].item(), - "loss_entropy": out["loss_entropy"].item(), - "nsec_acc": out["nsec_acc"].item(), - "batch_size": out["batch_size"], - } - - def train_mode(self) -> None: - self.model.train() - - def eval_mode(self) -> None: - self.model.eval() - - def state_dict(self) -> dict: - sd = { - "model": self.model.state_dict(), - "optimizer": self.optimizer.state_dict(), - "lr_sched": self.lr_sched.state_dict(), - } - if self.ema_model is not None: - sd["model_ema"] = self.ema_model.state_dict() - return sd - - def load_state_dict(self, sd: dict) -> None: - self.model.load_state_dict(sd["model"]) - self.optimizer.load_state_dict(sd["optimizer"]) - self.lr_sched.load_state_dict(sd["lr_sched"]) - if self.ema_model is not None: - self.ema_model.load_state_dict(sd.get("model_ema", sd["model"])) - - def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: - """Restore --lr's authority after load_state_dict restored the - checkpoint's own base LR (mirrors the resume fixup train() used to - do inline). `critic_lr` is unused here (no critic on this trainer), - kept only to match `StageTrainer.resume_lr`'s signature.""" - self.lr_sched.base_lrs = [lr for _ in self.lr_sched.base_lrs] - resumed_lr = lr * self._lr_lambda(self.lr_sched.last_epoch) - for group in self.optimizer.param_groups: - group["lr"] = resumed_lr - - -class WGANStageTrainer(StageTrainer): - """WGAN-GP generator+critic for a single stage (see giant/model/wgan.py). - - Ports `_wgan_train_step` to operate on one stage instead of two fused - together — the critic updates every batch; every `n_critic`-th batch - additionally updates the generator (`did_g_step`). The (non-adversarial) - n_sec classifier, when this stage's model owns it, updates every batch - regardless — folded into whichever generator optimizer step happens this - batch, same precedent as v0.2. - """ - - def __init__( - self, - name: str, - model: torch.nn.Module, - critic: torch.nn.Module, - is_stage2: bool, - lambda_weight: float, - n_sec_lambda: float, - n_critic: int, - gp_weight: float, - lr: float, - critic_lr: float, - ema_decay: float, - warmup_epochs: int, - epochs: int, - steps_per_epoch: int, - device: torch.device, - particle_type_cfg: dict | None = None, - particle_type_emb_dim: int = 16, - type_gumbel_tau_start: float = 1.0, - type_gumbel_tau_end: float = 0.1, - decoder: str = "one_shot", - teacher_forcing: str = "always", - tf_p_start: float = 1.0, - tf_p_end: float = 1.0, - ar_sample_steps: int = 10, - ) -> None: - self.name = name - self.is_stage2 = is_stage2 - self.decoder = decoder - self.teacher_forcing = teacher_forcing - self.tf_p_start = tf_p_start - self.tf_p_end = tf_p_end - self.ar_sample_steps = ar_sample_steps - self.total_epochs = epochs - self.steps_per_epoch = max(steps_per_epoch, 1) - self.device = device - self.model = model.to(device) - self.critic = critic.to(device) - self.lambda_weight = lambda_weight - self.n_sec_lambda = n_sec_lambda - self.n_critic = max(n_critic, 1) - self.gp_weight = gp_weight - self.ema_decay = ema_decay - self.router = _stage_router(self.model) - - self.particle_type_cfg = dict(particle_type_cfg or {"target": "physical"}) - self.particle_type_emb_dim = particle_type_emb_dim - self.type_gumbel_tau_start = type_gumbel_tau_start - self.type_gumbel_tau_end = type_gumbel_tau_end - - self.g_params = list(self.model.parameters()) - self.d_params = list(self.critic.parameters()) - # WGAN-GP recipe (Gulrajani et al. 2017): Adam, beta1=0, no weight decay. - self.optimizer = optim.Adam(self.g_params, lr=lr, betas=(0.0, 0.9)) - self.optimizer_d = optim.Adam( - self.d_params, lr=critic_lr if critic_lr > 0 else lr, betas=(0.0, 0.9) - ) - - # Generator steps fire every n_critic-th batch, so warmup/decay must - # be counted in those units, matching v0.2. - gen_steps_per_epoch = max(steps_per_epoch // self.n_critic, 1) - warmup_steps = warmup_epochs * gen_steps_per_epoch - total_steps = max(epochs * gen_steps_per_epoch, 1) - - def _lr_lambda(step: int) -> float: - if warmup_steps > 0 and step < warmup_steps: - return (step + 1) / warmup_steps - t = step - warmup_steps - T = max(total_steps - warmup_steps, 1) - return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) - - self._lr_lambda = _lr_lambda - self.lr_sched = optim.lr_scheduler.LambdaLR(self.optimizer, _lr_lambda) - self.total_steps = total_steps - - self.ema_model: torch.nn.Module | None = None - if ema_decay > 0: - self.ema_model = copy.deepcopy(self.model).eval() - for p in self.ema_model.parameters(): - p.requires_grad_(False) - - def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: - ( - cond_cont, - cond_cat, - x1_s1, - n_sec, - sec_cont, - _proc_idx, - sec_type_idx, - ) = _batch_to_device(batch, device) - B = cond_cont.size(0) - stage1_ctx = x1_s1.detach() - grad_probe: dict[str, float] = {} - - if not self.is_stage2: - real = x1_s1 - - def critic_fn(x): - return self.critic(x, cond_cont, cond_cat) - - z = torch.randn(B, self.model.noise_dim, device=device) - fake = self.model(z, cond_cont, cond_cat) - mask = None - else: - target = self.particle_type_cfg.get("target", "physical") - type_dim = stage2_type_dim( - self.particle_type_cfg, self.particle_type_emb_dim - ) - slot_width = CONT_SLOT_DIM + type_dim - k_max = sec_cont.size(1) - - sec_mask = torch.arange(k_max, device=device).unsqueeze( - 0 - ) < n_sec.unsqueeze(1) - mask = ( - sec_mask.unsqueeze(-1).expand(-1, -1, slot_width).reshape(B, -1).float() - ) - - def critic_fn(x): - return self.critic(x, cond_cont, cond_cat, stage1_ctx) - - if self.decoder == "autoregressive": - epoch = global_step // self.steps_per_epoch - p_tf = _stage2_tf_prob( - self.teacher_forcing, - self.tf_p_start, - self.tf_p_end, - epoch, - self.total_epochs, - ) - ar = _assemble_stage2_ar_inputs_scheduled( - self.model, - cond_cont, - cond_cat, - stage1_ctx, - sec_cont, - sec_type_idx, - n_sec, - self.particle_type_cfg, - self.model.cond_enc, - self.particle_type_emb_dim, - p_tf, - self.ar_sample_steps, - ) - real = ( - _assemble_stage2_ar_target( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - "wgan", - self.model.cond_enc, - self.particle_type_emb_dim, - ).reshape(B, -1) - * mask - ) - z = torch.randn(B, k_max, self.model.noise_dim, device=device) - fake_raw = self.model( - z, - cond_cont, - cond_cat, - stage1_ctx, - ar["history_feat"], - ar["has_prev"], - ar["remaining_frac"], - ar["slot_idx"], - ).reshape(B, -1) - else: - real = ( - _assemble_stage2_real( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - "wgan", - self.model.cond_enc, - self.particle_type_emb_dim, - ) - * mask - ) - z = torch.randn(B, self.model.noise_dim, device=device) - fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) - - if target == "onehot": - # Straight-through Gumbel-softmax relaxation of the type - # slice only (decision 5) — the critic must see a hard - # one-hot forward (matching what "real" data looks like) - # while gradient still flows smoothly to the generator. - # grad_probe captures the §11.4 gradient-magnitude - # instrumentation — see _relax_onehot_type_slice's docstring. - tau = _gumbel_tau( - global_step, - self.total_steps, - self.type_gumbel_tau_start, - self.type_gumbel_tau_end, - ) - fake_raw = _relax_onehot_type_slice( - fake_raw, k_max, CONT_SLOT_DIM, type_dim, tau, grad_probe=grad_probe - ) - fake = fake_raw * mask - - # --- critic step (every batch) --- - fake_detached = fake.detach() - real_score = critic_fn(real) - fake_score = critic_fn(fake_detached) - gp = gradient_penalty(critic_fn, real, fake_detached, mask=mask) - d_loss = fake_score.mean() - real_score.mean() + self.gp_weight * gp - wasserstein_estimate = (real_score.mean() - fake_score.mean()).detach() - - self.optimizer_d.zero_grad() - d_loss.backward() - grad_norm_d = torch.nn.utils.clip_grad_norm_(self.d_params, 1.0) - self.optimizer_d.step() - - # --- generator (+ n_sec) step --- - did_g_step = global_step % self.n_critic == 0 - n_sec_logits = None - if self.model.n_sec_head is not None: - n_sec_logits = ( - self.model.predict_n_sec(cond_cont, cond_cat) - if not self.is_stage2 - else self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) - ) - l_nsec = torch.zeros((), device=device) - nsec_acc = torch.zeros((), device=device) - if n_sec_logits is not None: - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() - - # On a non-generator-step batch with no n_sec_head on this stage - # (n_sec now defaults to stage 2, decision 1), there's nothing for - # the generator optimizer to do this batch — g_loss would otherwise - # be a graph-less zero tensor, which .backward() rejects outright. - skip_g_step = not did_g_step and n_sec_logits is None - if did_g_step: - g_loss_adv = generator_loss(critic_fn, fake) - g_loss = self.lambda_weight * g_loss_adv + self.n_sec_lambda * l_nsec - else: - g_loss_adv = torch.zeros((), device=device) - g_loss = self.n_sec_lambda * l_nsec - if skip_g_step: - grad_norm_g = torch.zeros(()) - else: - self.optimizer.zero_grad() - g_loss.backward() - grad_norm_g = torch.nn.utils.clip_grad_norm_(self.g_params, 1.0) - self.optimizer.step() - - if did_g_step: - self.lr_sched.step() - if self.ema_model is not None: - _update_ema(self.ema_model, self.model, self.ema_decay) - - return { - "d_loss": d_loss.item(), - "g_loss": g_loss_adv.item(), - "wasserstein_estimate": wasserstein_estimate.item(), - "gp_loss": gp.item(), - "loss_nsec": l_nsec.item(), - "nsec_acc": nsec_acc.item(), - "did_g_step": did_g_step, - "grad_norm": grad_norm_d.item() + grad_norm_g.item(), - "grad_norm_d": grad_norm_d.item(), - "grad_norm_g": grad_norm_g.item(), - "grad_norm_type_slice": grad_probe.get("type", 0.0), - "grad_norm_cont_slice": grad_probe.get("cont", 0.0), - "lr": self.optimizer.param_groups[0]["lr"], - "critic_lr": self.optimizer_d.param_groups[0]["lr"], - "batch_size": B, - } - - def val_loss(self, batch: tuple, device: torch.device) -> dict: - # No monotone per-batch WGAN loss fit for averaging; best-checkpoint - # selection instead uses validate_marginals (or its fallback) — see - # train()'s end-of-epoch block. - raise NotImplementedError - - def train_mode(self) -> None: - self.model.train() - self.critic.train() - - def eval_mode(self) -> None: - self.model.eval() - self.critic.eval() - - def state_dict(self) -> dict: - sd = { - "model": self.model.state_dict(), - "critic": self.critic.state_dict(), - "optimizer": self.optimizer.state_dict(), - "optimizer_d": self.optimizer_d.state_dict(), - "lr_sched": self.lr_sched.state_dict(), - } - if self.ema_model is not None: - sd["model_ema"] = self.ema_model.state_dict() - return sd - - def load_state_dict(self, sd: dict) -> None: - self.model.load_state_dict(sd["model"]) - self.critic.load_state_dict(sd["critic"]) - self.optimizer.load_state_dict(sd["optimizer"]) - self.optimizer_d.load_state_dict(sd["optimizer_d"]) - self.lr_sched.load_state_dict(sd["lr_sched"]) - if self.ema_model is not None: - self.ema_model.load_state_dict(sd.get("model_ema", sd["model"])) - - def resume_lr(self, lr: float, critic_lr: float = 0.0) -> None: - self.lr_sched.base_lrs = [lr for _ in self.lr_sched.base_lrs] - resumed_lr = lr * self._lr_lambda(self.lr_sched.last_epoch) - for group in self.optimizer.param_groups: - group["lr"] = resumed_lr - resumed_critic_lr = critic_lr if critic_lr > 0 else lr - for group in self.optimizer_d.param_groups: - group["lr"] = resumed_critic_lr - - -def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): - """Runs `validate_marginals` on `trainer`'s sampling model (EMA model if - present, else the raw model). `validate_marginals` itself dispatches - through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec` - (docs/v0.3.0-design.md §10), so this is generator- and - one-shot-vs-autoregressive-agnostic.""" - model = trainer.sampling_model() - return validate_marginals(model, val_loader, device=device, **kwargs) - - -def _build_stage_trainers( - cfg: dict, - models: dict[str, torch.nn.Module | None], - critics: dict[str, torch.nn.Module | None], - device: torch.device, - total_train_batches: int, -) -> dict[str, StageTrainer]: - t = cfg["train"] - trainers: dict[str, StageTrainer] = {} - for name, is_stage2 in (("stage1", False), ("stage2", True)): - model = models.get(name) - if model is None: - continue - stage_cfg = cfg[f"{name}_model"] - generator = stage_cfg["generator"] - lambda_weight = stage_cfg.get("lambda", 1.0) - n_sec_cfg = cfg["stage2_model"].get("n_sec", {}) - n_sec_lambda = n_sec_cfg.get("lambda", 0.1) - router_cfg = stage_cfg.get("router") or {} - lambda_balance = router_cfg.get("lambda_balance", 0.0) - lambda_proc = router_cfg.get("lambda_proc", 0.0) - lambda_entropy = router_cfg.get("lambda_entropy", 0.0) - gumbel_tau_start = router_cfg.get("gumbel_tau_start", 1.0) - gumbel_tau_end = router_cfg.get("gumbel_tau_end", 0.1) - particle_type_cfg = cfg["stage2_model"].get("particle_type") or { - "target": "physical" - } - particle_type_emb_dim = cfg["conditioning"]["particle"]["emb_dim"] - decoder = stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot" - ar_cfg = (stage_cfg.get("autoregressive") or {}) if is_stage2 else {} - teacher_forcing = ar_cfg.get("teacher_forcing", "always") - tf_p_start = ar_cfg.get("tf_p_start", 1.0) - tf_p_end = ar_cfg.get("tf_p_end", 1.0) - # AR self-sampling under scheduled/never teacher forcing reuses - # train.validate_steps as its flow-matching ODE step count — no - # dedicated config key for this (docs/v0.3.0-design.md §3.3 lists - # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). - ar_sample_steps = t.get("validate_steps", 10) - - if generator == "wgan": - critic = critics.get(name) - assert critic is not None, ( - f"{name}_model.generator='wgan' requires a critic (see " - "giant.model.network.build_critics)" - ) - wgan_cfg = stage_cfg.get("wgan", {}) - trainers[name] = WGANStageTrainer( - name=name, - model=model, - critic=critic, - is_stage2=is_stage2, - lambda_weight=lambda_weight, - n_sec_lambda=n_sec_lambda, - n_critic=wgan_cfg.get("n_critic", 5), - gp_weight=wgan_cfg.get("gp_weight", 10.0), - lr=t["lr"], - critic_lr=wgan_cfg.get("critic_lr", 0.0), - ema_decay=t.get("ema_decay", 0.9999), - warmup_epochs=t["warmup_epochs"], - epochs=t["epochs"], - steps_per_epoch=max(total_train_batches, 1), - device=device, - particle_type_cfg=particle_type_cfg, - particle_type_emb_dim=particle_type_emb_dim, - type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), - type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), - decoder=decoder, - teacher_forcing=teacher_forcing, - tf_p_start=tf_p_start, - tf_p_end=tf_p_end, - ar_sample_steps=ar_sample_steps, - ) - else: - ddpm_n_steps = stage_cfg.get("ddpm", {}).get("n_steps", 1000) - trainers[name] = FlowDDPMStageTrainer( - name=name, - model=model, - is_stage2=is_stage2, - generator=generator, - lambda_weight=lambda_weight, - n_sec_lambda=n_sec_lambda, - lambda_balance=lambda_balance, - lambda_proc=lambda_proc, - lambda_entropy=lambda_entropy, - gumbel_tau_start=gumbel_tau_start, - gumbel_tau_end=gumbel_tau_end, - lr=t["lr"], - weight_decay=t.get("weight_decay", 0.01), - ema_decay=t.get("ema_decay", 0.9999), - warmup_epochs=t["warmup_epochs"], - epochs=t["epochs"], - steps_per_epoch=max(total_train_batches, 1), - ddpm_n_steps=ddpm_n_steps, - device=device, - particle_type_cfg=particle_type_cfg, - particle_type_emb_dim=particle_type_emb_dim, - decoder=decoder, - teacher_forcing=teacher_forcing, - tf_p_start=tf_p_start, - tf_p_end=tf_p_end, - ar_sample_steps=ar_sample_steps, - ) - return trainers - - -def _metrics_fields(trainers: dict[str, StageTrainer]) -> list[str]: - fields = ["epoch"] - for name, trainer in trainers.items(): - is_wgan = isinstance(trainer, WGANStageTrainer) - if is_wgan: - fields += [ - f"{name}_train_d_loss", - f"{name}_train_g_loss", - f"{name}_train_wasserstein", - f"{name}_train_gp_loss", - f"{name}_train_loss_nsec", - f"{name}_train_nsec_acc", - f"{name}_train_grad_norm_d", - f"{name}_train_grad_norm_g", - ] - if ( - trainer.is_stage2 - and trainer.particle_type_cfg.get("target") == "onehot" - ): - fields += [ - f"{name}_train_grad_norm_type_slice", - f"{name}_train_grad_norm_cont_slice", - ] - else: - fields += [ - f"{name}_train_loss", - f"{name}_train_loss_gen", - f"{name}_train_loss_nsec", - f"{name}_train_loss_balance", - f"{name}_train_loss_proc", - f"{name}_train_loss_entropy", - f"{name}_train_nsec_acc", - f"{name}_train_loss_type", - f"{name}_train_type_acc", - f"{name}_train_grad_norm", - f"{name}_val_loss", - f"{name}_val_loss_gen", - f"{name}_val_loss_nsec", - f"{name}_val_nsec_acc", - f"{name}_val_loss_type", - f"{name}_val_type_acc", - ] - if trainer.router is not None: - fields += [ - f"{name}_router_entropy", - f"{name}_router_util_min", - f"{name}_router_util_max", - f"{name}_router_util_std", - ] - fields += [f"{name}_lr"] - if is_wgan: - fields += [f"{name}_critic_lr"] - fields += [ - "val_loss", - "val_marginal_kl", - "grad_norm", - "gpu_mem_mb", - "samples_per_sec", - "is_best", - "epoch_time_s", - ] - return fields - - -def _wandb_run_config(cfg: dict, model_config: dict | None, param_counts: dict) -> dict: - return { - "train": cfg["train"], - "conditioning": cfg["conditioning"], - "stage1_model": cfg["stage1_model"], - "stage2_model": cfg["stage2_model"], - "model_config": model_config or {}, - "param_counts": param_counts, - } - - -@torch.no_grad() -def _router_gate_stats(router, cond_cont, cond_cat): - return router.gate_stats(cond_cont, cond_cat) - - -def train( - cfg: dict, - models: dict[str, torch.nn.Module | None], - critics: dict[str, torch.nn.Module | None], - train_loader: DataLoader, - val_loader: DataLoader, - device: torch.device, - out_dir: str | Path, - normalizer_dict: dict | None = None, - pdg_map: dict | None = None, - mat_map: dict | None = None, - proc_map: dict | None = None, - pdg_topn_map: TopNMap | None = None, - mat_topn_map: TopNMap | None = None, - model_config: dict | None = None, - resume_path: str | Path | None = None, - total_train_batches: int = 0, - use_wandb: bool = False, - wandb_project: str = "giant", - wandb_run_name: str = "", - wandb_log_every: int = 50, -) -> None: - """Train whichever of stage1/stage2 are active, each through its own - `StageTrainer` (design doc §7). `models`/`critics` are the dicts - `giant.model.network.build_models`/`build_critics` return — a `None` - entry means that stage is `active = false`. - """ - out_dir = Path(out_dir) - out_dir.mkdir(parents=True, exist_ok=True) - t = cfg["train"] - epochs = t["epochs"] - validate_every = t.get("validate_every", 0) - validate_steps = t.get("validate_steps", 10) - max_val_batches = t.get("max_val_batches", 0) - - trainers = _build_stage_trainers(cfg, models, critics, device, total_train_batches) - if not trainers: - raise ValueError( - "no active stage — stage1_model.active and stage2_model.active are both false" - ) - - param_counts = { - name: sum(p.numel() for p in tr.model.parameters()) - for name, tr in trainers.items() - } - param_counts["total"] = sum(param_counts.values()) - - wandb_run = None - if use_wandb: - try: - import wandb - except ImportError as exc: - raise RuntimeError( - "train.wandb = true (--wandb) requires the 'wandb' package — " - "install it via `uv sync --extra wandb`" - ) from exc - wandb_run = wandb.init( - project=wandb_project, - name=wandb_run_name or out_dir.name, - id=out_dir.name, - resume="allow", - config=_wandb_run_config(cfg, model_config, param_counts), - ) - - def _build_checkpoint(epoch: int, global_step: int, best_val_loss: float) -> dict: - ckpt: dict = { - "epoch": epoch, - "best_val_loss": best_val_loss, - "global_step": global_step, - } - for name, tr in trainers.items(): - sd = tr.state_dict() - key = "model" if name == "stage1" else "sec_decoder" - ckpt[key] = sd["model"] - if "model_ema" in sd: - ckpt[f"{key}_ema"] = sd["model_ema"] - if "critic" in sd: - ckpt["critic" if name == "stage1" else "sec_critic"] = sd["critic"] - ckpt[f"optimizer_{name}"] = sd["optimizer"] - ckpt[f"lr_sched_{name}"] = sd["lr_sched"] - if "optimizer_d" in sd: - ckpt[f"optimizer_d_{name}"] = sd["optimizer_d"] - if normalizer_dict is not None: - ckpt["normalizer"] = normalizer_dict - if pdg_map is not None: - ckpt["pdg_map"] = pdg_map - if mat_map is not None: - ckpt["mat_map"] = mat_map - if proc_map is not None: - ckpt["proc_map"] = proc_map - if pdg_topn_map is not None: - ckpt["pdg_topn_map"] = topnmap_to_json(pdg_topn_map) - if mat_topn_map is not None: - ckpt["mat_topn_map"] = topnmap_to_json(mat_topn_map) - if model_config is not None: - ckpt["model_config"] = model_config - return ckpt - - def _load_checkpoint(ckpt: dict) -> None: - for name, tr in trainers.items(): - key = "model" if name == "stage1" else "sec_decoder" - sd = { - "model": ckpt[key], - "optimizer": ckpt[f"optimizer_{name}"], - "lr_sched": ckpt[f"lr_sched_{name}"], - } - ema_key = f"{key}_ema" - if ema_key in ckpt: - sd["model_ema"] = ckpt[ema_key] - crit_key = "critic" if name == "stage1" else "sec_critic" - if crit_key in ckpt: - sd["critic"] = ckpt[crit_key] - sd["optimizer_d"] = ckpt[f"optimizer_d_{name}"] - tr.load_state_dict(sd) - if isinstance(tr, WGANStageTrainer): - wgan_cfg = cfg[f"{name}_model"].get("wgan", {}) - tr.resume_lr(t["lr"], wgan_cfg.get("critic_lr", 0.0)) - else: - tr.resume_lr(t["lr"]) - - start_epoch = 1 - best_val_loss = float("inf") - resumed_global_step = 0 - if resume_path is not None: - ckpt = torch.load(resume_path, map_location=device, weights_only=False) - _load_checkpoint(ckpt) - start_epoch = ckpt.get("epoch", 0) + 1 - best_val_loss = ckpt.get("best_val_loss", float("inf")) - resumed_global_step = ckpt.get("global_step", 0) - if start_epoch > epochs: - print( - f"checkpoint already completed epoch {start_epoch - 1} " - f"(>= --epochs {epochs}) — nothing to train" - ) - return - - fields = _metrics_fields(trainers) - metrics_path = out_dir / "metrics.csv" - resuming_existing_metrics = resume_path is not None and metrics_path.exists() - write_header = not resuming_existing_metrics - metrics_file = open( - metrics_path, "a" if resuming_existing_metrics else "w", newline="" - ) - metrics_writer = csv.DictWriter(metrics_file, fieldnames=fields) - if write_header: - metrics_writer.writeheader() - - epoch_w = len(str(epochs)) - last_completed_epoch = start_epoch - 1 - global_step = resumed_global_step - with _GracefulShutdown() as shutdown: - for epoch in range(start_epoch, epochs + 1): - epoch_start = time.monotonic() - if device.type == "cuda": - torch.cuda.reset_peak_memory_stats(device) - for tr in trainers.values(): - tr.train_mode() - - train_sums: dict[str, dict[str, float]] = {n: {} for n in trainers} - train_n = 0 - ema_loss = 0.0 - ema_grad_norm = 0.0 - bar = tqdm( - train_loader, - desc=f" epoch {epoch:{epoch_w}d}/{epochs}", - total=total_train_batches or None, - leave=False, - unit="batch", - dynamic_ncols=True, - ) - for batch in bar: - batch_loss_total = 0.0 - batch_grad_norm_total = 0.0 - B = batch[0].size(0) - for name, tr in trainers.items(): - stats = tr.step(batch, device, global_step) - sums = train_sums[name] - for k, v in stats.items(): - if isinstance(v, bool): - continue - sums[k] = sums.get(k, 0.0) + v * B - if isinstance(tr, WGANStageTrainer): - batch_loss_total += stats["d_loss"] + stats["g_loss"] - batch_grad_norm_total += stats["grad_norm"] - else: - batch_loss_total += stats["loss"] - batch_grad_norm_total += stats["grad_norm"] - - train_n += B - ema_loss = ( - batch_loss_total - if train_n == B - else 0.95 * ema_loss + 0.05 * batch_loss_total - ) - ema_grad_norm = ( - batch_grad_norm_total - if train_n == B - else 0.95 * ema_grad_norm + 0.05 * batch_grad_norm_total - ) - bar.set_postfix_str( - f"loss={ema_loss:.4f} gnorm={ema_grad_norm:.3f}", refresh=False - ) - - global_step += 1 - if ( - wandb_run is not None - and wandb_log_every > 0 - and global_step % wandb_log_every == 0 - ): - log_payload = { - "batch/epoch": epoch, - "batch/loss": batch_loss_total, - "batch/loss_ema": ema_loss, - "batch/grad_norm": batch_grad_norm_total, - } - for name, tr in trainers.items(): - log_payload[f"batch/{name}_lr"] = tr.optimizer.param_groups[0][ - "lr" - ] - if tr.router is not None: - with torch.no_grad(): - cond_cont_b = batch[0].to(device) - cond_cat_b = batch[1].to(device) - entropy, _ = _router_gate_stats( - tr.router, cond_cont_b, cond_cat_b - ) - log_payload[f"batch/{name}_router_entropy"] = entropy.item() - wandb_run.log(log_payload, step=global_step) - - if shutdown.requested: - break - bar.close() - - if shutdown.requested: - ckpt = _build_checkpoint(epoch - 1, global_step, best_val_loss) - torch.save(ckpt, out_dir / "last.pt") - last_completed_epoch = epoch - 1 - print( - f"saved in-progress weights from partway through epoch " - f"{epoch} to {out_dir / 'last.pt'} " - f"(resume will restart epoch {epoch})" - ) - break - - for tr in trainers.values(): - tr.eval_mode() - - # --- per-stage validation --- - val_sums: dict[str, dict[str, float]] = {n: {} for n in trainers} - val_router_sums: dict[str, dict] = {} - val_n = 0 - non_adversarial = { - n: tr - for n, tr in trainers.items() - if not isinstance(tr, WGANStageTrainer) - } - if non_adversarial: - with torch.no_grad(): - for val_batch_idx, batch in enumerate(val_loader): - if max_val_batches > 0 and val_batch_idx >= max_val_batches: - break - B = batch[0].size(0) - for name, tr in non_adversarial.items(): - stats = tr.val_loss(batch, device) - sums = val_sums[name] - for k, v in stats.items(): - sums[k] = sums.get(k, 0.0) + v * B - if tr.router is not None: - cond_cont = batch[0].to(device) - cond_cat = batch[1].to(device) - entropy, importance = _router_gate_stats( - tr.router, cond_cont, cond_cat - ) - rs = val_router_sums.setdefault( - name, - { - "entropy": 0.0, - "importance": torch.zeros_like(importance), - "n_experts": tr.router.n_experts, - }, - ) - rs["entropy"] += entropy.item() * B - rs["importance"] += importance - val_n += B - - val_loss_per_stage: dict[str, float] = {} - for name, sums in val_sums.items(): - n = max(val_n, 1) - val_loss_per_stage[name] = sums.get("loss", 0.0) / n - - val_marginal_kl = float("nan") - wgan_names = [ - n for n, tr in trainers.items() if isinstance(tr, WGANStageTrainer) - ] - if wgan_names: - # No monotone per-batch WGAN loss for best-checkpoint - # selection — try the real marginal-KL signal (see - # _try_validate_marginals), falling back to the epoch's own - # Wasserstein-distance magnitude, averaged over active wgan - # stages, if that's unavailable. - stage1_tr = trainers.get("stage1") - kl_result = None - if stage1_tr is not None: - kl_result = _try_validate_marginals( - stage1_tr, - val_loader, - device, - sec_decoder=trainers["stage2"].sampling_model() - if "stage2" in trainers - else None, - ) - if kl_result is not None: - val_marginal_kl = float(np.mean(kl_result["kl_divergence"])) - val_loss_per_stage.update( - { - n: val_marginal_kl - for n in wgan_names - if n not in val_loss_per_stage - } - ) - else: - wasserstein_proxy = sum( - train_sums[n].get("wasserstein_estimate", 0.0) - for n in wgan_names - ) / max(train_n, 1) - for n in wgan_names: - val_loss_per_stage[n] = abs(wasserstein_proxy) - elif validate_every > 0 and epoch % validate_every == 0: - stage1_tr = trainers.get("stage1") - if stage1_tr is not None: - kl_result = _try_validate_marginals( - stage1_tr, - val_loader, - device, - steps=validate_steps, - ddpm_steps=stage1_tr.ddpm_schedule.T - if isinstance(stage1_tr, FlowDDPMStageTrainer) - and stage1_tr.ddpm_schedule is not None - else 1000, - sec_decoder=trainers["stage2"].sampling_model() - if "stage2" in trainers - else None, - ) - if kl_result is not None: - val_marginal_kl = float(np.mean(kl_result["kl_divergence"])) - - val_loss = ( - sum(val_loss_per_stage.values()) if val_loss_per_stage else float("nan") - ) - - epoch_time = time.monotonic() - epoch_start - gpu_mem_mb = ( - torch.cuda.max_memory_allocated(device) / (1024 * 1024) - if device.type == "cuda" - else 0.0 - ) - - is_best = val_loss < best_val_loss - marker = " [best]" if is_best else "" - summary_bits = [] - for name, tr in trainers.items(): - if isinstance(tr, WGANStageTrainer): - n = max(train_n, 1) - summary_bits.append( - f"{name}[d={train_sums[name].get('d_loss', 0.0) / n:.3f} " - f"g={train_sums[name].get('g_loss', 0.0) / n:.3f}]" - ) - else: - n = max(train_n, 1) - summary_bits.append( - f"{name}[loss={train_sums[name].get('loss', 0.0) / n:.3f}]" - ) - print( - f"epoch {epoch:{epoch_w}d}/{epochs} " - + " ".join(summary_bits) - + f" val {val_loss:.4f} {epoch_time:.1f}s{marker}" - ) - - metrics_row: dict = {"epoch": epoch} - grad_norm_total = 0.0 - for name, tr in trainers.items(): - n_train = max(train_n, 1) - sums = train_sums[name] - if isinstance(tr, WGANStageTrainer): - metrics_row[f"{name}_train_d_loss"] = ( - sums.get("d_loss", 0.0) / n_train - ) - metrics_row[f"{name}_train_g_loss"] = ( - sums.get("g_loss", 0.0) / n_train - ) - metrics_row[f"{name}_train_wasserstein"] = ( - sums.get("wasserstein_estimate", 0.0) / n_train - ) - metrics_row[f"{name}_train_gp_loss"] = ( - sums.get("gp_loss", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_nsec"] = ( - sums.get("loss_nsec", 0.0) / n_train - ) - metrics_row[f"{name}_train_nsec_acc"] = ( - sums.get("nsec_acc", 0.0) / n_train - ) - metrics_row[f"{name}_train_grad_norm_d"] = ( - sums.get("grad_norm_d", 0.0) / n_train - ) - metrics_row[f"{name}_train_grad_norm_g"] = ( - sums.get("grad_norm_g", 0.0) / n_train - ) - if tr.is_stage2 and tr.particle_type_cfg.get("target") == "onehot": - metrics_row[f"{name}_train_grad_norm_type_slice"] = ( - sums.get("grad_norm_type_slice", 0.0) / n_train - ) - metrics_row[f"{name}_train_grad_norm_cont_slice"] = ( - sums.get("grad_norm_cont_slice", 0.0) / n_train - ) - grad_norm_total += sums.get("grad_norm", 0.0) / n_train - metrics_row[f"{name}_critic_lr"] = tr.optimizer_d.param_groups[0][ - "lr" - ] - else: - n_val = max(val_n, 1) - v = val_sums.get(name, {}) - metrics_row[f"{name}_train_loss"] = sums.get("loss", 0.0) / n_train - metrics_row[f"{name}_train_loss_gen"] = ( - sums.get("loss_gen", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_nsec"] = ( - sums.get("loss_nsec", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_balance"] = ( - sums.get("loss_balance", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_proc"] = ( - sums.get("loss_proc", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_entropy"] = ( - sums.get("loss_entropy", 0.0) / n_train - ) - metrics_row[f"{name}_train_nsec_acc"] = ( - sums.get("nsec_acc", 0.0) / n_train - ) - metrics_row[f"{name}_train_loss_type"] = ( - sums.get("loss_type", 0.0) / n_train - ) - metrics_row[f"{name}_train_type_acc"] = ( - sums.get("type_acc", 0.0) / n_train - ) - metrics_row[f"{name}_train_grad_norm"] = ( - sums.get("grad_norm", 0.0) / n_train - ) - metrics_row[f"{name}_val_loss"] = v.get("loss", 0.0) / n_val - metrics_row[f"{name}_val_loss_gen"] = v.get("loss_gen", 0.0) / n_val - metrics_row[f"{name}_val_loss_nsec"] = ( - v.get("loss_nsec", 0.0) / n_val - ) - metrics_row[f"{name}_val_nsec_acc"] = v.get("nsec_acc", 0.0) / n_val - metrics_row[f"{name}_val_loss_type"] = ( - v.get("loss_type", 0.0) / n_val - ) - metrics_row[f"{name}_val_type_acc"] = v.get("type_acc", 0.0) / n_val - grad_norm_total += sums.get("grad_norm", 0.0) / n_train - if tr.router is not None: - rs = val_router_sums.get(name) - if rs is not None: - n_experts = rs["n_experts"] - util = rs["importance"] / rs["importance"].sum().clamp_min(1e-8) - metrics_row[f"{name}_router_entropy"] = rs["entropy"] / max( - val_n, 1 - ) - metrics_row[f"{name}_router_util_min"] = util.min().item() - metrics_row[f"{name}_router_util_max"] = util.max().item() - metrics_row[f"{name}_router_util_std"] = ( - util.std().item() if n_experts > 1 else 0.0 - ) - else: - metrics_row[f"{name}_router_entropy"] = 0.0 - metrics_row[f"{name}_router_util_min"] = 0.0 - metrics_row[f"{name}_router_util_max"] = 0.0 - metrics_row[f"{name}_router_util_std"] = 0.0 - metrics_row[f"{name}_lr"] = tr.optimizer.param_groups[0]["lr"] - - metrics_row["val_loss"] = val_loss - metrics_row["val_marginal_kl"] = val_marginal_kl - metrics_row["grad_norm"] = grad_norm_total - metrics_row["gpu_mem_mb"] = gpu_mem_mb - metrics_row["samples_per_sec"] = train_n / max(epoch_time, 1e-8) - metrics_row["is_best"] = int(is_best) - metrics_row["epoch_time_s"] = epoch_time - - metrics_writer.writerow(metrics_row) - metrics_file.flush() - if wandb_run is not None: - wandb_run.log(metrics_row, step=global_step) - - ckpt = _build_checkpoint(epoch, global_step, best_val_loss) - if is_best: - best_val_loss = val_loss - ckpt["best_val_loss"] = best_val_loss - torch.save(ckpt, out_dir / "best.pt") - torch.save(ckpt, out_dir / "last.pt") - last_completed_epoch = epoch - - if shutdown.requested: - break - - metrics_file.close() - if wandb_run is not None: - wandb_run.finish() - - if shutdown.requested: - print( - f"stopped after epoch {last_completed_epoch} due to shutdown signal — " - f"resume with --resume {out_dir / 'last.pt'}" - ) diff --git a/giant/training/__init__.py b/giant/training/__init__.py new file mode 100644 index 0000000..ed50b5f --- /dev/null +++ b/giant/training/__init__.py @@ -0,0 +1,30 @@ +"""Training: per-stage trainers, metric collection, checkpointing, the loop. + +Split out of the former single-module `giant/train.py`. The public surface is +`train` (the entry point `giant.pipeline` calls) plus the trainer/spec types +that tests and tooling construct directly. +""" + +from giant.training.checkpoint import build_checkpoint, load_checkpoint +from giant.training.metrics import MetricsCollector, MetricSpec +from giant.training.loop import train +from giant.training.trainers import ( + FlowDDPMStageTrainer, + StageSpec, + StageTrainer, + WGANStageTrainer, + build_stage_trainers, +) + +__all__ = [ + "FlowDDPMStageTrainer", + "MetricSpec", + "MetricsCollector", + "StageSpec", + "StageTrainer", + "WGANStageTrainer", + "build_checkpoint", + "build_stage_trainers", + "load_checkpoint", + "train", +] diff --git a/giant/training/checkpoint.py b/giant/training/checkpoint.py new file mode 100644 index 0000000..c946314 --- /dev/null +++ b/giant/training/checkpoint.py @@ -0,0 +1,68 @@ +"""Checkpoint assembly and restore. + +The on-disk layout is unchanged from v0.2/v0.3.0 and is read by +`giant/cli.py`, `giant/rollout.py`, `giant/sample.py` and +`giant/analysis/router_gating.py` — stage 1's weights live under `model`, +stage 2's under `sec_decoder`, with `_ema`/`critic`/`sec_critic` companions +and per-stage `optimizer_` / `optimizer_d_` / `lr_sched_` +entries. +""" + +from giant.training.trainers import StageTrainer + +#: Stage name -> the checkpoint key its weights live under. Historical: stage +#: 1 predates the two-stage split, so it kept the bare "model" key. +_STAGE_KEY = {"stage1": "model", "stage2": "sec_decoder"} +_CRITIC_KEY = {"stage1": "critic", "stage2": "sec_critic"} + + +def build_checkpoint( + trainers: dict[str, StageTrainer], + epoch: int, + global_step: int, + best_val_loss: float, + extras: dict, +) -> dict: + """`extras` carries the dataset-level sidecars (normalizer, vocab maps, + model_config) that `train()` receives as arguments; `None` values are + omitted so an absent sidecar leaves no key behind.""" + ckpt: dict = { + "epoch": epoch, + "best_val_loss": best_val_loss, + "global_step": global_step, + } + for name, trainer in trainers.items(): + sd = trainer.state_dict() + key = _STAGE_KEY[name] + ckpt[key] = sd["model"] + if "model_ema" in sd: + ckpt[f"{key}_ema"] = sd["model_ema"] + if "critic" in sd: + ckpt[_CRITIC_KEY[name]] = sd["critic"] + ckpt[f"optimizer_d_{name}"] = sd["optimizer_d"] + ckpt[f"optimizer_{name}"] = sd["optimizer"] + ckpt[f"lr_sched_{name}"] = sd["lr_sched"] + ckpt.update({k: v for k, v in extras.items() if v is not None}) + return ckpt + + +def load_checkpoint(trainers: dict[str, StageTrainer], ckpt: dict, lr: float) -> None: + """Restore every active stage, then hand `lr`'s authority back to the + config — `load_state_dict` would otherwise leave the checkpoint's own + base LR in place, silently ignoring `--lr` on resume.""" + for name, trainer in trainers.items(): + key = _STAGE_KEY[name] + sd = { + "model": ckpt[key], + "optimizer": ckpt[f"optimizer_{name}"], + "lr_sched": ckpt[f"lr_sched_{name}"], + } + ema_key = f"{key}_ema" + if ema_key in ckpt: + sd["model_ema"] = ckpt[ema_key] + crit_key = _CRITIC_KEY[name] + if crit_key in ckpt: + sd["critic"] = ckpt[crit_key] + sd["optimizer_d"] = ckpt[f"optimizer_d_{name}"] + trainer.load_state_dict(sd) + trainer.resume_lr(lr) diff --git a/giant/training/loop.py b/giant/training/loop.py new file mode 100644 index 0000000..39719cc --- /dev/null +++ b/giant/training/loop.py @@ -0,0 +1,328 @@ +"""The training loop. + +`train()` owns the epoch structure and nothing else: the per-stage step is +`giant.training.trainers`' job, every number reported is +`giant.training.metrics`' job, and the on-disk checkpoint is +`giant.training.checkpoint`'s. +""" + +import os +import signal +import time +from pathlib import Path +from types import FrameType +from typing import Callable + +import numpy as np +import torch +from torch.utils.data import DataLoader +from tqdm import tqdm + +from giant.data.loader import TopNMap +from giant.data.setup_cache import topnmap_to_json +from giant.training.checkpoint import build_checkpoint, load_checkpoint +from giant.training.metrics import MetricsCollector +from giant.training.trainers import ( + FlowDDPMStageTrainer, + StageTrainer, + build_stage_trainers, +) +from giant.validate import validate_marginals + +_CATCHABLE_SIGNALS = (signal.SIGINT, signal.SIGTERM) + + +class _GracefulShutdown: + """Turns SIGINT/SIGTERM into a flag check instead of an immediate crash. + + A second signal while already shutting down restores the default + handler and re-sends the signal, so an unresponsive run can still be + force-killed. + """ + + def __init__(self) -> None: + self.requested = False + self._previous: dict[ + int, + Callable[[int, FrameType | None], object] | signal.Handlers | int | None, + ] = {} + + def __enter__(self) -> "_GracefulShutdown": + for sig in _CATCHABLE_SIGNALS: + self._previous[sig] = signal.getsignal(sig) + signal.signal(sig, self._handle) + return self + + def __exit__(self, *exc_info) -> None: + for sig, handler in self._previous.items(): + signal.signal(sig, handler) + + def _handle(self, signum: int, frame) -> None: + if self.requested: + signal.signal(signum, self._previous[signum]) + os.kill(os.getpid(), signum) + return + self.requested = True + print( + f"\nreceived {signal.Signals(signum).name} — finishing the current " + "batch, then saving a checkpoint and exiting (send again to force-quit)" + ) + + +def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): + """Runs `validate_marginals` on `trainer`'s sampling model (EMA model if + present, else the raw model). `validate_marginals` itself dispatches + through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec` + (docs/v0.3.0-design.md §10), so this is generator- and + one-shot-vs-autoregressive-agnostic.""" + model = trainer.sampling_model() + return validate_marginals(model, val_loader, device=device, **kwargs) + + +def _marginal_kl( + trainers: dict[str, StageTrainer], val_loader, device, **kwargs +) -> float: + """Mean marginal KL over the stage-1 sampling chain, or NaN when stage 1 + is inactive or `validate_marginals` declined to produce a result.""" + stage1 = trainers.get("stage1") + if stage1 is None: + return float("nan") + result = _try_validate_marginals( + stage1, + val_loader, + device, + sec_decoder=trainers["stage2"].sampling_model() + if "stage2" in trainers + else None, + **kwargs, + ) + if result is None: + return float("nan") + return float(np.mean(result["kl_divergence"])) + + +def train( + cfg: dict, + models: dict[str, torch.nn.Module | None], + critics: dict[str, torch.nn.Module | None], + train_loader: DataLoader, + val_loader: DataLoader, + device: torch.device, + out_dir: str | Path, + normalizer_dict: dict | None = None, + pdg_map: dict | None = None, + mat_map: dict | None = None, + proc_map: dict | None = None, + pdg_topn_map: TopNMap | None = None, + mat_topn_map: TopNMap | None = None, + model_config: dict | None = None, + resume_path: str | Path | None = None, + total_train_batches: int = 0, + use_wandb: bool = False, + wandb_project: str = "giant", + wandb_run_name: str = "", + wandb_log_every: int = 50, +) -> None: + """Train whichever of stage1/stage2 are active, each through its own + `StageTrainer` (design doc §7). `models`/`critics` are the dicts + `giant.model.network.build_models`/`build_critics` return — a `None` + entry means that stage is `active = false`. + """ + out_dir = Path(out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + t = cfg["train"] + epochs = t["epochs"] + validate_every = t.get("validate_every", 0) + validate_steps = t.get("validate_steps", 10) + max_val_batches = t.get("max_val_batches", 0) + + trainers = build_stage_trainers(cfg, models, critics, device, total_train_batches) + if not trainers: + raise ValueError( + "no active stage — stage1_model.active and stage2_model.active are both false" + ) + has_adversarial = any(not tr.supports_val_loss for tr in trainers.values()) + + checkpoint_extras = { + "normalizer": normalizer_dict, + "pdg_map": pdg_map, + "mat_map": mat_map, + "proc_map": proc_map, + "pdg_topn_map": topnmap_to_json(pdg_topn_map) + if pdg_topn_map is not None + else None, + "mat_topn_map": topnmap_to_json(mat_topn_map) + if mat_topn_map is not None + else None, + "model_config": model_config, + } + + start_epoch = 1 + best_val_loss = float("inf") + global_step = 0 + if resume_path is not None: + ckpt = torch.load(resume_path, map_location=device, weights_only=False) + load_checkpoint(trainers, ckpt, t["lr"]) + start_epoch = ckpt.get("epoch", 0) + 1 + best_val_loss = ckpt.get("best_val_loss", float("inf")) + global_step = ckpt.get("global_step", 0) + if start_epoch > epochs: + print( + f"checkpoint already completed epoch {start_epoch - 1} " + f"(>= --epochs {epochs}) — nothing to train" + ) + return + + collector = MetricsCollector.create( + trainers, + out_dir, + cfg, + model_config, + resume=resume_path is not None, + use_wandb=use_wandb, + wandb_project=wandb_project, + wandb_run_name=wandb_run_name, + wandb_log_every=wandb_log_every, + ) + + epoch_w = len(str(epochs)) + last_completed_epoch = start_epoch - 1 + with _GracefulShutdown() as shutdown: + for epoch in range(start_epoch, epochs + 1): + epoch_start = time.monotonic() + if device.type == "cuda": + torch.cuda.reset_peak_memory_stats(device) + collector.start_epoch(epoch) + for trainer in trainers.values(): + trainer.train_mode() + + bar = tqdm( + train_loader, + desc=f" epoch {epoch:{epoch_w}d}/{epochs}", + total=total_train_batches or None, + leave=False, + unit="batch", + dynamic_ncols=True, + ) + for batch in bar: + B = batch[0].size(0) + collector.add_train_batch( + { + name: trainer.step(batch, device, global_step) + for name, trainer in trainers.items() + }, + B, + ) + bar.set_postfix_str(collector.postfix(), refresh=False) + global_step += 1 + collector.log_batch(global_step, batch, device) + if shutdown.requested: + break + bar.close() + + if shutdown.requested: + ckpt = build_checkpoint( + trainers, epoch - 1, global_step, best_val_loss, checkpoint_extras + ) + torch.save(ckpt, out_dir / "last.pt") + last_completed_epoch = epoch - 1 + print( + f"saved in-progress weights from partway through epoch " + f"{epoch} to {out_dir / 'last.pt'} " + f"(resume will restart epoch {epoch})" + ) + break + + for trainer in trainers.values(): + trainer.eval_mode() + + # --- per-stage validation --- + scored = {name: tr for name, tr in trainers.items() if tr.supports_val_loss} + if scored: + with torch.no_grad(): + for val_batch_idx, batch in enumerate(val_loader): + if max_val_batches > 0 and val_batch_idx >= max_val_batches: + break + B = batch[0].size(0) + collector.add_val_batch( + { + name: tr.val_loss(batch, device) + for name, tr in scored.items() + }, + B, + ) + collector.observe_routers( + batch[0].to(device), batch[1].to(device), B + ) + + # An adversarial stage has no averageable validation loss, so it + # needs the marginal-KL signal every epoch to pick a best + # checkpoint at all; a purely non-adversarial run only pays for + # it every `validate_every` epochs. + marginal_kl = float("nan") + if has_adversarial: + marginal_kl = _marginal_kl(trainers, val_loader, device) + elif validate_every > 0 and epoch % validate_every == 0: + stage1 = trainers.get("stage1") + ddpm_steps = 1000 + if ( + isinstance(stage1, FlowDDPMStageTrainer) + and stage1.ddpm_schedule is not None + ): + ddpm_steps = stage1.ddpm_schedule.T + marginal_kl = _marginal_kl( + trainers, + val_loader, + device, + steps=validate_steps, + ddpm_steps=ddpm_steps, + ) + + val_loss = sum( + trainer.val_objective( + collector.train_means(name), + collector.val_means(name), + marginal_kl, + ) + for name, trainer in trainers.items() + ) + + epoch_time = time.monotonic() - epoch_start + is_best = val_loss < best_val_loss + collector.set("val/loss", val_loss) + collector.set("val/marginal_kl", marginal_kl) + collector.set( + "gpu_mem_mb", + torch.cuda.max_memory_allocated(device) / (1024 * 1024) + if device.type == "cuda" + else 0.0, + ) + collector.set( + "samples_per_sec", collector.train_samples / max(epoch_time, 1e-8) + ) + collector.set("is_best", int(is_best)) + collector.set("epoch_time_s", epoch_time) + + print(collector.summary_line(val_loss, epoch_time, is_best)) + collector.write_epoch(global_step) + + ckpt = build_checkpoint( + trainers, epoch, global_step, best_val_loss, checkpoint_extras + ) + if is_best: + best_val_loss = val_loss + ckpt["best_val_loss"] = best_val_loss + torch.save(ckpt, out_dir / "best.pt") + torch.save(ckpt, out_dir / "last.pt") + last_completed_epoch = epoch + + if shutdown.requested: + break + + collector.close() + + if shutdown.requested: + print( + f"stopped after epoch {last_completed_epoch} due to shutdown signal — " + f"resume with --resume {out_dir / 'last.pt'}" + ) diff --git a/giant/training/metrics.py b/giant/training/metrics.py new file mode 100644 index 0000000..42975ee --- /dev/null +++ b/giant/training/metrics.py @@ -0,0 +1,405 @@ +"""Per-epoch metric accumulation, `metrics.csv`, and W&B logging. + +Every scalar a training run reports is declared exactly once, as a +`MetricSpec` on the `StageTrainer` that computes it (see +`giant.training.trainers`). `MetricsCollector` derives the CSV/W&B column set +from those declarations, so adding a metric means adding one line next to the +code that produces it — there is no second list to keep in sync. + +Column naming is uniform: `/train/`, `/val/`, +`/` for point-in-time values (`lr`, `critic_lr`), +`/router/` for routing diagnostics, and an unprefixed run-level +tail (`val/loss`, `grad_norm`, `epoch_time_s`, ...). W&B groups panels on +`/`, so the same names read well there. +""" + +import csv +from dataclasses import dataclass +from pathlib import Path + +import torch + +_ROUTER_KEYS = ("entropy", "util_min", "util_max", "util_std") + +# Written after every stage's columns, by `MetricsCollector` itself rather +# than by any one trainer — these describe the run, not a stage. +_RUN_COLUMNS = ( + "val/loss", + "val/marginal_kl", + "grad_norm", + "gpu_mem_mb", + "samples_per_sec", + "is_best", + "epoch_time_s", +) + +# tqdm/W&B batch-granularity smoothing, matching v0.2/v0.3.0's inline EMA. +_EMA_ALPHA = 0.05 + + +@dataclass(frozen=True) +class MetricSpec: + """One scalar a trainer emits per batch, and how it is reported. + + `key` indexes the dict `StageTrainer.step()` / `.val_loss()` returns; + `column` is the CSV/W&B column suffix, joined to the stage name with + "/". `reduce` is either "mean" (batch-size-weighted average over the + epoch) or "last" (the most recent value — for point-in-time quantities + like the learning rate, which is a schedule readout, not a statistic). + """ + + key: str + column: str + reduce: str = "mean" + + +def train_metric(key: str, column: str | None = None) -> MetricSpec: + return MetricSpec(key, column or f"train/{key}") + + +def val_metric(key: str, column: str | None = None) -> MetricSpec: + return MetricSpec(key, column or f"val/{key}") + + +def stage_metric(key: str, column: str | None = None) -> MetricSpec: + """A point-in-time stage-level readout (`lr`, `critic_lr`) — reported + unprefixed by split, as `/`.""" + return MetricSpec(key, column or key, reduce="last") + + +def _wandb_run_config(cfg: dict, model_config: dict | None, param_counts: dict) -> dict: + return { + "train": cfg["train"], + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], + "model_config": model_config or {}, + "param_counts": param_counts, + } + + +class _Accumulator: + """Batch-size-weighted sums for one stage and one split.""" + + def __init__(self) -> None: + self.sums: dict[str, float] = {} + self.n = 0 + self.last: dict[str, float] = {} + + def add(self, stats: dict, keys: set[str], batch_size: int) -> None: + for key in keys: + if key in stats: + self.sums[key] = self.sums.get(key, 0.0) + stats[key] * batch_size + self.last.update(stats) + self.n += batch_size + + def mean(self, key: str) -> float: + return self.sums.get(key, 0.0) / max(self.n, 1) + + def means(self) -> dict[str, float]: + return {key: self.mean(key) for key in self.sums} + + def reset(self) -> None: + self.sums.clear() + self.last.clear() + self.n = 0 + + +class _RouterAccumulator: + """Gate-diagnostic sums for one routed stage.""" + + def __init__(self, n_experts: int) -> None: + self.n_experts = n_experts + self.entropy = 0.0 + self.importance: torch.Tensor | None = None + self.n = 0 + + def add(self, entropy: torch.Tensor, importance: torch.Tensor, n: int) -> None: + self.entropy += entropy.item() * n + self.importance = ( + importance.clone() + if self.importance is None + else self.importance + importance + ) + self.n += n + + def stats(self) -> dict[str, float]: + if self.importance is None or self.n == 0: + return dict.fromkeys(_ROUTER_KEYS, 0.0) + util = self.importance / self.importance.sum().clamp_min(1e-8) + return { + "entropy": self.entropy / self.n, + "util_min": util.min().item(), + "util_max": util.max().item(), + "util_std": util.std().item() if self.n_experts > 1 else 0.0, + } + + def reset(self) -> None: + self.entropy = 0.0 + self.importance = None + self.n = 0 + + +class MetricsCollector: + """Owns every number a training run reports. + + Accumulates per-batch stats from each stage, writes one `metrics.csv` row + per epoch, mirrors it to W&B, and formats the tqdm postfix and the epoch + summary line — so `giant.training.loop.train` never carries a running + sum, a column name, or a W&B call of its own. + """ + + def __init__( + self, + trainers: dict, + out_dir: Path, + *, + epochs: int, + resume: bool = False, + wandb_run=None, + wandb_log_every: int = 50, + ) -> None: + self.trainers = trainers + self.epochs = epochs + self.wandb_run = wandb_run + self.wandb_log_every = wandb_log_every + self.epoch_width = len(str(epochs)) + + self._train = {name: _Accumulator() for name in trainers} + self._val = {name: _Accumulator() for name in trainers} + self._routers = { + name: _RouterAccumulator(tr.router.n_experts) + for name, tr in trainers.items() + if tr.router is not None + } + # Only "mean" specs need summing; "last" specs are read straight off + # the accumulator's most recent stats dict. "grad_norm" is always + # summed — it feeds the run-level `grad_norm` column whether or not + # a trainer reports it per stage. + self._train_keys = { + name: {spec.key for spec in tr.train_metrics if spec.reduce == "mean"} + | {"grad_norm"} + for name, tr in trainers.items() + } + self._val_keys = { + name: {spec.key for spec in tr.val_metrics if spec.reduce == "mean"} + for name, tr in trainers.items() + } + self._run_values: dict[str, float] = {} + self._epoch = 0 + self._ema_loss = 0.0 + self._ema_grad_norm = 0.0 + self._ema_seeded = False + self._batch_loss = 0.0 + self._batch_grad_norm = 0.0 + + self.fieldnames = self._build_fieldnames() + metrics_path = out_dir / "metrics.csv" + append = resume and metrics_path.exists() + self._file = open(metrics_path, "a" if append else "w", newline="") + self._writer = csv.DictWriter(self._file, fieldnames=self.fieldnames) + if not append: + self._writer.writeheader() + + # --- construction --------------------------------------------------- + + @classmethod + def create( + cls, + trainers: dict, + out_dir: Path, + cfg: dict, + model_config: dict | None, + *, + resume: bool = False, + use_wandb: bool = False, + wandb_project: str = "giant", + wandb_run_name: str = "", + wandb_log_every: int = 50, + ) -> "MetricsCollector": + """Build the collector, starting a W&B run first when enabled.""" + wandb_run = None + if use_wandb: + try: + import wandb + except ImportError as exc: + raise RuntimeError( + "train.wandb = true (--wandb) requires the 'wandb' package — " + "install it via `uv sync --extra wandb`" + ) from exc + param_counts = { + name: sum(p.numel() for p in tr.model.parameters()) + for name, tr in trainers.items() + } + param_counts["total"] = sum(param_counts.values()) + wandb_run = wandb.init( + project=wandb_project, + name=wandb_run_name or out_dir.name, + id=out_dir.name, + resume="allow", + config=_wandb_run_config(cfg, model_config, param_counts), + ) + return cls( + trainers, + out_dir, + epochs=cfg["train"]["epochs"], + resume=resume, + wandb_run=wandb_run, + wandb_log_every=wandb_log_every, + ) + + def _build_fieldnames(self) -> list[str]: + fields = ["epoch"] + for name, trainer in self.trainers.items(): + for spec in trainer.train_metrics: + fields.append(f"{name}/{spec.column}") + for spec in trainer.val_metrics: + fields.append(f"{name}/{spec.column}") + if trainer.router is not None: + fields += [f"{name}/router/{key}" for key in _ROUTER_KEYS] + for spec in trainer.stage_metrics: + fields.append(f"{name}/{spec.column}") + fields += list(_RUN_COLUMNS) + return fields + + def close(self) -> None: + self._file.close() + if self.wandb_run is not None: + self.wandb_run.finish() + + # --- per-batch ------------------------------------------------------ + + def start_epoch(self, epoch: int) -> None: + self._epoch = epoch + for acc in self._train.values(): + acc.reset() + for acc in self._val.values(): + acc.reset() + for acc in self._routers.values(): + acc.reset() + self._run_values.clear() + self._ema_seeded = False + + def add_train_batch(self, stats: dict[str, dict], batch_size: int) -> None: + """`stats` maps stage name -> the dict that stage's `step()` returned.""" + self._batch_loss = 0.0 + self._batch_grad_norm = 0.0 + for name, stage_stats in stats.items(): + self._train[name].add(stage_stats, self._train_keys[name], batch_size) + self._batch_loss += self.trainers[name].batch_loss(stage_stats) + self._batch_grad_norm += stage_stats.get("grad_norm", 0.0) + if self._ema_seeded: + self._ema_loss += _EMA_ALPHA * (self._batch_loss - self._ema_loss) + self._ema_grad_norm += _EMA_ALPHA * ( + self._batch_grad_norm - self._ema_grad_norm + ) + else: + self._ema_loss = self._batch_loss + self._ema_grad_norm = self._batch_grad_norm + self._ema_seeded = True + + def add_val_batch(self, stats: dict[str, dict], batch_size: int) -> None: + for name, stage_stats in stats.items(): + self._val[name].add(stage_stats, self._val_keys[name], batch_size) + + @torch.no_grad() + def observe_routers( + self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, batch_size: int + ) -> None: + """Record gate diagnostics for every routed stage on this batch. + + Called from the validation pass only (as in v0.2/v0.3.0), so a stage + whose trainer has no validation pass — i.e. WGAN — reports zeros. + """ + for name, acc in self._routers.items(): + router = self.trainers[name].router + entropy, importance = router.gate_stats(cond_cont, cond_cat) + acc.add(entropy, importance, batch_size) + + def postfix(self) -> str: + """tqdm postfix for the training bar.""" + return f"loss={self._ema_loss:.4f} gnorm={self._ema_grad_norm:.3f}" + + def log_batch(self, global_step: int, batch: tuple, device: torch.device) -> None: + """Batch-granularity W&B log, throttled to every `wandb_log_every` + optimizer steps (a single epoch can be tens of thousands). `batch` is + the raw training batch, needed only to re-derive routing entropy for + routed stages — it is never moved to `device` otherwise.""" + if self.wandb_run is None or self.wandb_log_every <= 0: + return + if global_step % self.wandb_log_every != 0: + return + payload = { + "batch/epoch": self._epoch, + "batch/loss": self._batch_loss, + "batch/loss_ema": self._ema_loss, + "batch/grad_norm": self._batch_grad_norm, + } + for name, trainer in self.trainers.items(): + payload[f"batch/{name}/lr"] = trainer.optimizer.param_groups[0]["lr"] + if trainer.router is not None: + with torch.no_grad(): + entropy, _ = trainer.router.gate_stats( + batch[0].to(device), batch[1].to(device) + ) + payload[f"batch/{name}/router/entropy"] = entropy.item() + self.wandb_run.log(payload, step=global_step) + + # --- per-epoch ------------------------------------------------------ + + def train_means(self, stage: str) -> dict[str, float]: + return self._train[stage].means() + + def val_means(self, stage: str) -> dict[str, float]: + return self._val[stage].means() + + @property + def train_samples(self) -> int: + """Samples seen this epoch — identical across stages (every stage + steps on every batch), so any one accumulator's count will do.""" + return max((acc.n for acc in self._train.values()), default=0) + + def set(self, column: str, value: float) -> None: + """Record a run-level value for this epoch's row (`val/loss`, + `gpu_mem_mb`, ...). Must name a column in `_RUN_COLUMNS`.""" + if column not in _RUN_COLUMNS: + raise KeyError(f"{column!r} is not a run-level metrics column") + self._run_values[column] = value + + def summary_line(self, val_loss: float, epoch_time: float, is_best: bool) -> str: + bits = [ + trainer.summary(self.train_means(name)) + for name, trainer in self.trainers.items() + ] + marker = " [best]" if is_best else "" + return ( + f"epoch {self._epoch:{self.epoch_width}d}/{self.epochs} " + + " ".join(bits) + + f" val {val_loss:.4f} {epoch_time:.1f}s{marker}" + ) + + def write_epoch(self, global_step: int) -> None: + """Assemble, write, and flush this epoch's row; mirror it to W&B.""" + row: dict = {"epoch": self._epoch} + grad_norm_total = 0.0 + for name, trainer in self.trainers.items(): + train_acc, val_acc = self._train[name], self._val[name] + for spec in trainer.train_metrics: + row[f"{name}/{spec.column}"] = train_acc.mean(spec.key) + for spec in trainer.val_metrics: + row[f"{name}/{spec.column}"] = val_acc.mean(spec.key) + if trainer.router is not None: + for key, value in self._routers[name].stats().items(): + row[f"{name}/router/{key}"] = value + for spec in trainer.stage_metrics: + row[f"{name}/{spec.column}"] = train_acc.last.get(spec.key, 0.0) + grad_norm_total += train_acc.mean("grad_norm") + + for column in _RUN_COLUMNS: + row[column] = self._run_values.get(column, float("nan")) + row["grad_norm"] = grad_norm_total + + self._writer.writerow(row) + self._file.flush() + if self.wandb_run is not None: + self.wandb_run.log(row, step=global_step) diff --git a/giant/training/stage2_inputs.py b/giant/training/stage2_inputs.py new file mode 100644 index 0000000..58f8908 --- /dev/null +++ b/giant/training/stage2_inputs.py @@ -0,0 +1,321 @@ +"""Ground-truth tensor assembly for stage-2 training. + +Pure functions, no optimizer/model state: they turn a batch's ground-truth +secondary tensors into the per-token targets and autoregressive conditioning +inputs `giant.training.trainers` feeds to `Stage2OneShot` / +`Stage2Autoregressive`. Split out of the trainers so the (target, generator, +decoder) width rules — the fiddliest part of docs/v0.3.0-design.md §2.1/§6 — +live in one place and stay unit-testable on their own. +""" + +import torch +import torch.nn.functional as F + +from giant.constants import CONT_SLOT_DIM, PARTICLE_PHYS_DIM +from giant.sample import sample_secondaries_ar + + +def _gumbel_tau(step: int, total_steps: int, tau_start: float, tau_end: float) -> float: + """Linear anneal of the straight-through Gumbel-softmax temperature. + + Deterministic in `step`/`total_steps` alone (no extra state), so it + recomputes correctly on `--resume` from a checkpoint's saved `global_step` + without needing to persist anything new (see + giant.model.network.Router.combine_weights). + """ + progress = min(step / max(total_steps, 1), 1.0) + return tau_start + (tau_end - tau_start) * progress + + +def _type_repr( + sec_type_idx: torch.Tensor, + sec_cont: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """(B, K_MAX, type_dim) ground-truth type representation, generator- + independent (unlike `_assemble_stage2_ar_target`'s training *target*, + which varies by generator/objective — see its docstring): `"physical"` -> + `(log_mass, charge)`; `"onehot"` -> one-hot of the true class; + `"embedding"` -> the conditioning's own detached embedding-table row. + + Used both to build `_assemble_stage2_ar_target`'s wgan+onehot/embedding + branch and as the AR history features' previous-secondary identity — the + latter must always reflect the true physical secondary that came before, + regardless of what the *current* token's own training objective is. + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM] + if target == "onehot": + return F.one_hot(sec_type_idx, num_classes=emb_dim).float() + return cond_enc.pdg_emb(sec_type_idx).detach() + + +def _assemble_stage2_ar_target( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + generator: str, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """(B, K_MAX, token_dim) ground-truth per-token target — the unflattened + analogue of `_assemble_stage2_real` (defined below in terms of this), + matching whatever width `Stage2Autoregressive`'s (or `Stage2OneShot`'s) + own trunk produces for this (target, generator) combination + (`giant.model.network.stage2_trunk_sec_dim`; docs/v0.3.0-design.md + decision 2/3): + + - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, + dir, log_mass, charge) as-is. + - `target` in `("onehot", "embedding")` + `generator in ("flow", "ddpm")`: + just the continuous stick/dir slots — the type slice isn't part of + this tensor at all (`type_head` handles it separately). + - `target` in `("onehot", "embedding")` + `generator == "wgan"`: stick/dir + slots concatenated with the per-slot type representation (a one-hot of + the true class, relaxed on the *generated* side only, by the caller; + or the conditioning's own detached embedding-table row). + """ + target = particle_type_cfg.get("target", "physical") + if target == "physical": + return sec_cont + cont = sec_cont[..., :CONT_SLOT_DIM] + if generator != "wgan": + return cont + type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) + return torch.cat([cont, type_repr], dim=-1) + + +def _assemble_stage2_real( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + generator: str, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> torch.Tensor: + """Ground-truth flattened stage-2 vector for `Stage2OneShot` — the + flattened form of `_assemble_stage2_ar_target`, which + `Stage2Autoregressive`'s per-token target also uses; the two must stay in + lockstep. See `_assemble_stage2_ar_target`'s docstring for the + (target, generator) width rules.""" + return _assemble_stage2_ar_target( + sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim + ).flatten(1) + + +def _stick_fraction(sec_cont: torch.Tensor) -> torch.Tensor: + """(B, K_MAX) — sigmoid of each slot's own stick-breaking logit + (`sec_cont[...,0]`); scale-free (see `giant.data.transforms. + encode_secondaries`), so this needs no absolute `e_sec`.""" + return torch.sigmoid(sec_cont[..., 0]) + + +def _remaining_energy_fraction(fraction: torch.Tensor) -> torch.Tensor: + """(B, K_MAX) — fraction of the original e_sec budget unclaimed entering + slot i: `1.0` at `i=0`, `prod_{j=1` + (docs/v0.3.0-design.md §6.3 — "no re-derivation needed": the existing + stick-breaking encoding is already scale-free, so this is derivable from + the batch's ground-truth stick logits alone, no `e_sec` required).""" + cumprod = torch.cumprod(1.0 - fraction, dim=1) + return torch.cat([torch.ones_like(cumprod[:, :1]), cumprod[:, :-1]], dim=1) + + +def _shift_prev(x: torch.Tensor) -> torch.Tensor: + """`(B, K, ...)` -> same shape, slot i holds slot i-1's value; slot 0 gets + an arbitrary zero placeholder (never read as-is — see `_ar_has_prev`; + `MarkovHistory` substitutes its own learned start vector there instead).""" + return torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1) + + +def _ar_has_prev(k_max: int, device: torch.device) -> torch.Tensor: + """`(1, K_MAX)` bool: True for slot index `>= 1`. Correct without + `n_sec`: `sec_mask` is a prefix mask, so any *valid* token at `k>=1` + always has a valid predecessor at `k-1`; the only wrong cases are tokens + that are themselves padding, already masked out of every loss.""" + return (torch.arange(k_max, device=device) >= 1).unsqueeze(0) + + +def _assemble_stage2_ar_inputs( + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, +) -> dict[str, torch.Tensor]: + """Ground-truth per-token AR conditioning tensors — all `(B, K_MAX, ...)` + or `(B, K_MAX)`, built in one vectorized pass (teacher forcing means + every token's input is ground truth, docs/v0.3.0-design.md §6.2 point 3). + Keys match `Stage2Autoregressive.forward`'s trailing kwargs.""" + device = sec_cont.device + B, K = sec_cont.shape[0], sec_cont.shape[1] + fraction = _stick_fraction(sec_cont) + type_repr = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) + history_feat = torch.cat( + [ + _shift_prev(fraction).unsqueeze(-1), + _shift_prev(sec_cont[..., 1:CONT_SLOT_DIM]), + _shift_prev(type_repr), + ], + dim=-1, + ) + slot_idx = (torch.arange(K, device=device).float() / max(K - 1, 1)).unsqueeze(0) + return { + "history_feat": history_feat, + "has_prev": _ar_has_prev(K, device).expand(B, -1), + "remaining_frac": _remaining_energy_fraction(fraction), + "slot_idx": slot_idx.expand(B, -1), + } + + +def _stage2_tf_prob( + mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int +) -> float: + """P(condition slot k+1 on the TRUE token k rather than the model's own + prediction), for the current epoch (docs/v0.3.0-design.md §3.3 + `stage2_model.autoregressive.teacher_forcing`). `"always"`/`"never"` are + the two degenerate constants; `"scheduled"` linearly interpolates + `p_start` (epoch 0) to `p_end` (the final epoch) — standard scheduled + sampling (Bengio et al. 2015).""" + if mode == "always": + return 1.0 + if mode == "never": + return 0.0 + frac = epoch / max(total_epochs - 1, 1) + frac = min(max(frac, 0.0), 1.0) + return p_start + (p_end - p_start) * frac + + +def _history_repr_from_ar_sample( + sec_cont_pred: torch.Tensor, + sec_type_pred: torch.Tensor, + particle_type_cfg: dict, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """`(fraction, direction, type_repr)` — the same triple `_type_repr` / + `_stick_fraction` derive from ground truth, but from a free-running + `sample_secondaries_ar` self-sample instead, so the two can be mixed + slot-by-slot under scheduled sampling (`_assemble_stage2_ar_inputs_scheduled`). + `target="onehot"` collapses the raw per-slot type logits to a hard + one-hot of `argmax` — `sample_secondaries_ar`'s own history convention + (see its docstring), matching what `MarkovHistory`/`AttentionHistory` + were trained on; the other two targets are already the right + representation.""" + fraction = torch.sigmoid(sec_cont_pred[..., 0]) + direction = sec_cont_pred[..., 1:4] + if particle_type_cfg.get("target", "physical") == "onehot": + type_dim = sec_type_pred.size(-1) + type_repr = F.one_hot(sec_type_pred.argmax(-1), num_classes=type_dim).float() + else: + type_repr = sec_type_pred + return fraction, direction, type_repr + + +def _assemble_stage2_ar_inputs_scheduled( + model: torch.nn.Module, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_ctx: torch.Tensor, + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + n_sec: torch.Tensor, + particle_type_cfg: dict, + cond_enc: torch.nn.Module, + emb_dim: int, + p_tf: float, + sample_steps: int, +) -> dict[str, torch.Tensor]: + """Scheduled-sampling counterpart of `_assemble_stage2_ar_inputs` + (docs/v0.3.0-design.md §3.3 `teacher_forcing` = "scheduled"/"never"): + each slot's history is the TRUE previous token with probability `p_tf` + (an independent per-example, per-slot Bernoulli draw) and the model's own + free-running prediction otherwise — closing the train/inference gap that + `teacher_forcing="always"` (ground truth throughout training) never sees. + `p_tf >= 1.0` degenerates exactly to `_assemble_stage2_ar_inputs` (and + skips self-sampling entirely), so callers can call this unconditionally. + + The free-running estimate is a REAL autoregressive self-sample — + `giant.sample.sample_secondaries_ar` under `torch.no_grad()` — not a + cheap one-step proxy, so building it costs the same `k_max` (`* steps` + for flow) sequential forwards `sample.py` pays at inference, EVERY batch + this is called on (§6.4's cost note, paid at train time too whenever + teacher_forcing != "always"). Fully detached: gradient only ever flows + through the "real" target path each stage trainer already uses + (`_assemble_stage2_ar_target`), never through this self-sample. + """ + device = sec_cont.device + B, K = sec_cont.shape[0], sec_cont.shape[1] + if p_tf >= 1.0: + return _assemble_stage2_ar_inputs( + sec_cont, sec_type_idx, particle_type_cfg, cond_enc, emb_dim + ) + + was_training = model.training + sec_cont_pred, sec_type_pred, _ = sample_secondaries_ar( + model, cond_cont, cond_cat, stage1_ctx, n_sec, steps=sample_steps + ) + if was_training: + model.train() + + fraction_gt = _stick_fraction(sec_cont) + dir_gt = sec_cont[..., 1:4] + type_repr_gt = _type_repr( + sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim + ) + fraction_pred, dir_pred, type_repr_pred = _history_repr_from_ar_sample( + sec_cont_pred, sec_type_pred, particle_type_cfg + ) + + use_gt = torch.rand(B, K, device=device) < p_tf + fraction = torch.where(use_gt, fraction_gt, fraction_pred) + direction = torch.where(use_gt.unsqueeze(-1), dir_gt, dir_pred) + type_repr = torch.where(use_gt.unsqueeze(-1), type_repr_gt, type_repr_pred) + + own_feat = torch.cat([fraction.unsqueeze(-1), direction, type_repr], dim=-1) + return { + "history_feat": _shift_prev(own_feat), + "has_prev": _ar_has_prev(K, device).expand(B, -1), + "remaining_frac": _remaining_energy_fraction(fraction), + "slot_idx": (torch.arange(K, device=device).float() / max(K - 1, 1)) + .unsqueeze(0) + .expand(B, -1), + } + + +def _relax_onehot_type_slice( + x_flat: torch.Tensor, + k_max: int, + cont_dim: int, + type_dim: int, + tau: float, + grad_probe: dict[str, float] | None = None, +) -> torch.Tensor: + """Straight-through Gumbel-softmax relaxation of the per-slot type slice + inside a flattened `(B, k_max * (cont_dim + type_dim))` WGAN generator + output — decision 5 (docs/v0.3.0-design.md §2.1): the forward pass is a + hard one-hot (matching what the critic sees from real data), the + backward pass flows smooth gradient. Continuous slots (stick/dir, and + the type slice itself under `target = "embedding"`, which never calls + this) pass through unchanged. + + `grad_probe`, if given, gets `["cont"]`/`["type"]` populated with the L2 + norm of the gradient reaching this split point during the next + `.backward()` call that touches it — a backward hook, not a second + backward pass. This is the §11.4 differentiability validation-obligation + instrumentation (docs/v0.3.0-design.md): the trunk-gradient contribution + from the type slice vs. the continuous slices, for + `particle_type.target="onehot"` + `generator="wgan"`. Only ever populated + on a `did_g_step` batch — the critic step backprops through + `fake.detach()`, which never reaches these hooks — so it stays empty + (callers default to `0.0`) otherwise.""" + B = x_flat.size(0) + x = x_flat.view(B, k_max, cont_dim + type_dim) + cont, type_logits = x[..., :cont_dim], x[..., cont_dim:] + if grad_probe is not None: + cont.register_hook(lambda g: grad_probe.__setitem__("cont", g.norm().item())) + type_logits.register_hook( + lambda g: grad_probe.__setitem__("type", g.norm().item()) + ) + type_soft = F.gumbel_softmax(type_logits, tau=tau, hard=True, dim=-1) + return torch.cat([cont, type_soft], dim=-1).reshape(B, -1) diff --git a/giant/training/trainers.py b/giant/training/trainers.py new file mode 100644 index 0000000..5bfc78f --- /dev/null +++ b/giant/training/trainers.py @@ -0,0 +1,987 @@ +"""Per-stage trainers: optimizer(s), EMA, LR schedule, and the per-batch step. + +`StageSpec` resolves one stage's slice of the config once, so the two +concrete trainers share a single constructor shape instead of ~24 keyword +arguments each, and `StageTrainer` carries every piece that used to be +copy-pasted between them (cosine warmup, EMA, checkpoint state, LR resume, +train/eval toggling). + +Each trainer also *declares* the metrics it emits, as `MetricSpec` lists — +that declaration is the single source of truth for `metrics.csv` and W&B +columns (see `giant.training.metrics`) — and exposes the three small hooks +(`batch_loss`, `summary`, `val_objective`) that let the epoch loop treat +adversarial and non-adversarial stages identically. +""" + +import copy +import math +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F +import torch.optim as optim + +from giant.constants import CONT_SLOT_DIM +from giant.model.network import Router, stage2_type_dim +from giant.model.schedule import ( + CosineSchedule, + flow_matching_loss, + flow_matching_loss_secondary, + flow_matching_loss_secondary_ar, +) +from giant.model.wgan import generator_loss, gradient_penalty +from giant.training.metrics import MetricSpec, stage_metric, train_metric, val_metric +from giant.training.stage2_inputs import ( + _assemble_stage2_ar_inputs_scheduled, + _assemble_stage2_ar_target, + _assemble_stage2_real, + _gumbel_tau, + _relax_onehot_type_slice, + _stage2_tf_prob, +) + + +@torch.no_grad() +def _update_ema( + ema_model: torch.nn.Module, model: torch.nn.Module, decay: float +) -> None: + for ema_p, p in zip(ema_model.parameters(), model.parameters()): + ema_p.mul_(decay).add_(p, alpha=1 - decay) + + +def _stage_router(model: torch.nn.Module) -> Router | None: + """A stage model's Router, if its trunk is routed — else None. + + Post-step-2 refactor the router lives at `model.trunk.router` + (`giant.model.network.RoutedTrunk`), not `model.router` directly. + """ + trunk = getattr(model, "trunk", None) + return getattr(trunk, "router", None) + + +def _cosine_warmup_lambda(warmup_steps: int, total_steps: int): + """Linear warmup for `warmup_steps`, then cosine decay to zero over the + remainder — the LR schedule both trainers use, in their own step units + (optimizer steps for flow/ddpm, generator steps for WGAN).""" + + def _lr_lambda(step: int) -> float: + if warmup_steps > 0 and step < warmup_steps: + return (step + 1) / warmup_steps + t = step - warmup_steps + T = max(total_steps - warmup_steps, 1) + return 0.5 * (1.0 + math.cos(math.pi * min(t, T) / T)) + + return _lr_lambda + + +def _batch_to_device(batch: tuple, device: torch.device) -> tuple: + return tuple(t.to(device) for t in batch) + + +@dataclass(frozen=True) +class StageSpec: + """One stage's resolved training configuration. + + Built once by `StageSpec.from_config`, which is the only place that reads + the `cfg` dict — so a new config key means one new field and one new read, + not another argument threaded through two constructors. + """ + + name: str + is_stage2: bool + generator: str + decoder: str = "one_shot" + + # loss weights + lambda_weight: float = 1.0 + n_sec_lambda: float = 0.1 + + # particle-type target (stage 2 only) + particle_type: dict = field(default_factory=lambda: {"target": "physical"}) + particle_type_emb_dim: int = 16 + + # optimization + lr: float = 3e-4 + weight_decay: float = 0.01 + ema_decay: float = 0.9999 + warmup_epochs: int = 0 + epochs: int = 1 + steps_per_epoch: int = 1 + + # routing auxiliaries + lambda_balance: float = 0.0 + lambda_proc: float = 0.0 + lambda_entropy: float = 0.0 + gumbel_tau_start: float = 1.0 + gumbel_tau_end: float = 0.1 + + # autoregressive stage 2 + teacher_forcing: str = "always" + tf_p_start: float = 1.0 + tf_p_end: float = 1.0 + ar_sample_steps: int = 10 + + # generator-specific + ddpm_n_steps: int = 1000 + n_critic: int = 5 + gp_weight: float = 10.0 + critic_lr: float = 0.0 + type_gumbel_tau_start: float = 1.0 + type_gumbel_tau_end: float = 0.1 + + @classmethod + def from_config( + cls, cfg: dict, name: str, is_stage2: bool, steps_per_epoch: int + ) -> "StageSpec": + t = cfg["train"] + stage_cfg = cfg[f"{name}_model"] + router_cfg = stage_cfg.get("router") or {} + wgan_cfg = stage_cfg.get("wgan") or {} + ar_cfg = (stage_cfg.get("autoregressive") or {}) if is_stage2 else {} + return cls( + name=name, + is_stage2=is_stage2, + generator=stage_cfg["generator"], + decoder=stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot", + lambda_weight=stage_cfg.get("lambda", 1.0), + n_sec_lambda=cfg["stage2_model"].get("n_sec", {}).get("lambda", 0.1), + particle_type=cfg["stage2_model"].get("particle_type") + or {"target": "physical"}, + particle_type_emb_dim=cfg["conditioning"]["particle"]["emb_dim"], + lr=t["lr"], + weight_decay=t.get("weight_decay", 0.01), + ema_decay=t.get("ema_decay", 0.9999), + warmup_epochs=t["warmup_epochs"], + epochs=t["epochs"], + steps_per_epoch=max(steps_per_epoch, 1), + lambda_balance=router_cfg.get("lambda_balance", 0.0), + lambda_proc=router_cfg.get("lambda_proc", 0.0), + lambda_entropy=router_cfg.get("lambda_entropy", 0.0), + gumbel_tau_start=router_cfg.get("gumbel_tau_start", 1.0), + gumbel_tau_end=router_cfg.get("gumbel_tau_end", 0.1), + teacher_forcing=ar_cfg.get("teacher_forcing", "always"), + tf_p_start=ar_cfg.get("tf_p_start", 1.0), + tf_p_end=ar_cfg.get("tf_p_end", 1.0), + # AR self-sampling under scheduled/never teacher forcing reuses + # train.validate_steps as its flow-matching ODE step count — no + # dedicated config key for this (docs/v0.3.0-design.md §3.3 lists + # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). + ar_sample_steps=t.get("validate_steps", 10), + ddpm_n_steps=stage_cfg.get("ddpm", {}).get("n_steps", 1000), + n_critic=wgan_cfg.get("n_critic", 5), + gp_weight=wgan_cfg.get("gp_weight", 10.0), + critic_lr=wgan_cfg.get("critic_lr", 0.0), + type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), + type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), + ) + + +class StageTrainer: + """One active stage's optimizer(s), EMA, and per-batch step. + + Reads only the shared batch tuple `(cond_cont, cond_cat, x1_s1, n_sec, + sec_cont, proc_idx, sec_type_idx)` — stage 2 always conditions on the + ground-truth `x1_s1` (`stage2_model.stage1_context = "truth"`, + stage-level teacher forcing; `"sampled"` is not implemented — see + docs/v0.3.0-design.md §3.3), so stage trainers never need each other's + output at train time. This means "stage-2-only training is a cheap + ablation, not new plumbing" (design doc §7) falls out for free: a trainer + only exists for active stages, and inactive stages are simply never + constructed. + + Grad-norm clipping is per-stage here — v0.2's single shared optimizer + clipped both stages' gradients jointly; splitting per stage is a small, + disclosed behavior change. It doesn't affect Adam's per-parameter update + math itself (no cross-parameter coupling), only the clip threshold's + scope. + """ + + #: Metrics this trainer emits, declared once — `giant.training.metrics` + #: derives every CSV/W&B column from these. Instance attributes rather + #: than class constants because some are conditional on the stage's own + #: configuration (see `WGANStageTrainer.__init__`). + train_metrics: list[MetricSpec] + val_metrics: list[MetricSpec] + stage_metrics: list[MetricSpec] + + #: False for adversarial stages, which have no monotone per-batch + #: validation loss worth averaging (see `val_objective`). + supports_val_loss: bool = True + + #: Built by the subclass (the optimizer flavour differs) and wired to the + #: schedule via `_init_lr_schedule`. + optimizer: optim.Optimizer + lr_sched: optim.lr_scheduler.LambdaLR + total_steps: int + + def __init__( + self, + spec: StageSpec, + model: torch.nn.Module, + device: torch.device, + extra_modules: tuple[torch.nn.Module, ...] = (), + ) -> None: + self.spec = spec + self.name = spec.name + self.is_stage2 = spec.is_stage2 + self.generator = spec.generator + self.decoder = spec.decoder + self.device = device + self.model = model.to(device) + self.router = _stage_router(self.model) + self._modules = (self.model, *extra_modules) + + self.particle_type_cfg = dict(spec.particle_type or {"target": "physical"}) + self.particle_type_emb_dim = spec.particle_type_emb_dim + self.ema_decay = spec.ema_decay + + self.ema_model: torch.nn.Module | None = None + if spec.ema_decay > 0: + self.ema_model = copy.deepcopy(self.model).eval() + for p in self.ema_model.parameters(): + p.requires_grad_(False) + + # --- schedule ------------------------------------------------------- + + def _init_lr_schedule( + self, optimizer: optim.Optimizer, warmup_steps: int, total_steps: int + ) -> None: + self._lr_lambda = _cosine_warmup_lambda(warmup_steps, total_steps) + self.total_steps = total_steps + self.lr_sched = optim.lr_scheduler.LambdaLR(optimizer, self._lr_lambda) + + # --- per-batch (subclass responsibility) ---------------------------- + + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + raise NotImplementedError + + def val_loss(self, batch: tuple, device: torch.device) -> dict: + raise NotImplementedError + + # --- reporting hooks ------------------------------------------------ + + def batch_loss(self, stats: dict) -> float: + """The single number this stage contributes to the progress bar's + smoothed loss.""" + raise NotImplementedError + + def summary(self, means: dict) -> str: + """This stage's fragment of the end-of-epoch console line.""" + raise NotImplementedError + + def val_objective( + self, train_means: dict, val_means: dict, marginal_kl: float + ) -> float: + """This stage's contribution to the best-checkpoint selection score.""" + raise NotImplementedError + + # --- mode / state --------------------------------------------------- + + def sampling_model(self) -> torch.nn.Module: + return self.ema_model if self.ema_model is not None else self.model + + def train_mode(self) -> None: + for module in self._modules: + module.train() + + def eval_mode(self) -> None: + for module in self._modules: + module.eval() + + def _extra_state(self) -> dict: + """Subclass state beyond model/optimizer/lr_sched/EMA.""" + return {} + + def _load_extra_state(self, sd: dict) -> None: + return None + + def state_dict(self) -> dict: + sd = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "lr_sched": self.lr_sched.state_dict(), + } + if self.ema_model is not None: + sd["model_ema"] = self.ema_model.state_dict() + sd.update(self._extra_state()) + return sd + + def load_state_dict(self, sd: dict) -> None: + self.model.load_state_dict(sd["model"]) + self.optimizer.load_state_dict(sd["optimizer"]) + self.lr_sched.load_state_dict(sd["lr_sched"]) + if self.ema_model is not None: + self.ema_model.load_state_dict(sd.get("model_ema", sd["model"])) + self._load_extra_state(sd) + + def _resume_extra_lr(self, lr: float) -> None: + return None + + def resume_lr(self, lr: float) -> None: + """Restore the configured `lr`'s authority after `load_state_dict` + restored the checkpoint's own base LR.""" + self.lr_sched.base_lrs = [lr for _ in self.lr_sched.base_lrs] + resumed_lr = lr * self._lr_lambda(self.lr_sched.last_epoch) + for group in self.optimizer.param_groups: + group["lr"] = resumed_lr + self._resume_extra_lr(lr) + + +class FlowDDPMStageTrainer(StageTrainer): + """flow or ddpm generator for a single stage.""" + + def __init__( + self, spec: StageSpec, model: torch.nn.Module, device: torch.device + ) -> None: + if spec.is_stage2 and spec.generator not in ("flow",): + raise NotImplementedError( + f"stage2_model.generator={spec.generator!r} is accepted by the " + "schema but not implemented in v0.3.0 for stage 2 (only " + "'flow' and 'wgan' have a stage-2 secondary-decoder loss — " + "see docs/v0.3.0-design.md §11.2)" + ) + super().__init__(spec, model, device) + self.particle_type_lambda = self.particle_type_cfg.get("lambda", 1.0) + # Width of the type slice actually folded into x1_s2 by + # _assemble_stage2_real, under this trainer's generator (flow/ddpm + # only — see the NotImplementedError above): "physical" keeps it + # folded in (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/ + # "embedding" pull it out into model.type_head instead (0 here). + self._flow_type_dim = ( + None + if self.particle_type_cfg.get("target", "physical") == "physical" + else 0 + ) + + self.params = list(self.model.parameters()) + self.optimizer = optim.AdamW( + self.params, lr=spec.lr, weight_decay=spec.weight_decay + ) + self._init_lr_schedule( + self.optimizer, + warmup_steps=spec.warmup_epochs * spec.steps_per_epoch, + total_steps=max(spec.epochs * spec.steps_per_epoch, 1), + ) + self.ddpm_schedule = ( + CosineSchedule(T=spec.ddpm_n_steps).to(device) + if spec.generator == "ddpm" + else None + ) + + self.train_metrics = [ + train_metric(key) + for key in ( + "loss", + "loss_gen", + "loss_nsec", + "loss_balance", + "loss_proc", + "loss_entropy", + "nsec_acc", + "loss_type", + "type_acc", + "grad_norm", + ) + ] + self.val_metrics = [ + val_metric(key) + for key in ( + "loss", + "loss_gen", + "loss_nsec", + "nsec_acc", + "loss_type", + "type_acc", + ) + ] + self.stage_metrics = [stage_metric("lr")] + + def _predict_n_sec(self, cond_cont, cond_cat, stage1_ctx): + if self.model.n_sec_head is None: + return None + if self.is_stage2: + return self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) + return self.model.predict_n_sec(cond_cont, cond_cat) + + def _generator_loss( + self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None + ): + if not self.is_stage2: + if self.generator == "flow": + return flow_matching_loss(self.model, x1_s1, cond_cont, cond_cat) + assert self.ddpm_schedule is not None + return self.ddpm_schedule.loss(self.model, x1_s1, cond_cont, cond_cat) + if self.decoder == "autoregressive": + assert ar_inputs is not None + return flow_matching_loss_secondary_ar( + self.model, + x1_s2, + cond_cont, + cond_cat, + stage1_ctx, + ar_inputs["history_feat"], + ar_inputs["has_prev"], + ar_inputs["remaining_frac"], + ar_inputs["slot_idx"], + sec_mask, + type_dim=self._flow_type_dim, + ) + return flow_matching_loss_secondary( + self.model, + x1_s2, + cond_cont, + cond_cat, + stage1_ctx, + sec_mask, + type_dim=self._flow_type_dim, + ) + + def _type_loss( + self, + cond_cont, + cond_cat, + stage1_ctx, + sec_type_idx, + sec_mask, + device, + ar_inputs=None, + ): + """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for the + stage-2 model's `type_head` — the non-adversarial counterpart to + WGANStageTrainer's ST-Gumbel-into-the-critic path (decision 2/5). + Zero when this stage has no `type_head` (stage 1, or + `particle_type.target = "physical"`).""" + l_type = torch.zeros((), device=device) + type_acc = torch.zeros((), device=device) + type_head = getattr(self.model, "type_head", None) + if not self.is_stage2 or type_head is None: + return l_type, type_acc + if self.decoder == "autoregressive": + assert ar_inputs is not None + type_out = self.model.predict_type( + cond_cont, + cond_cat, + stage1_ctx, + ar_inputs["history_feat"], + ar_inputs["has_prev"], + ar_inputs["remaining_frac"], + ar_inputs["slot_idx"], + ) + else: + type_out = self.model.predict_type(cond_cont, cond_cat, stage1_ctx) + mask = sec_mask.float() + denom = mask.sum().clamp(min=1) + if self.particle_type_cfg.get("target") == "onehot": + ce = F.cross_entropy( + type_out.transpose(1, 2), sec_type_idx, reduction="none" + ) + l_type = (ce * mask).sum() / denom + type_acc = ( + (type_out.argmax(-1) == sec_type_idx).float() * mask + ).sum() / denom + else: # "embedding" + target_vec = self.model.cond_enc.pdg_emb(sec_type_idx).detach() + se = ((type_out - target_vec) ** 2).mean(-1) + l_type = (se * mask).sum() / denom + return l_type, type_acc + + def _compute( + self, batch: tuple, device: torch.device, epoch: int | None = None + ) -> dict: + """`epoch=None` (the `val_loss` path) always uses full teacher + forcing (`p_tf=1.0`) regardless of `spec.teacher_forcing` — validation + should stay a stable, non-stochastic ground-truth comparison; only + the training `step` path schedules `p_tf` by epoch.""" + ( + cond_cont, + cond_cat, + x1_s1, + n_sec, + sec_cont, + proc_idx, + sec_type_idx, + ) = _batch_to_device(batch, device) + sec_mask = torch.arange(sec_cont.size(1), device=device).unsqueeze( + 0 + ) < n_sec.unsqueeze(1) + stage1_ctx = x1_s1.detach() + + x1_s2 = None + ar_inputs = None + if self.is_stage2 and self.decoder == "autoregressive": + p_tf = ( + 1.0 + if epoch is None + else _stage2_tf_prob( + self.spec.teacher_forcing, + self.spec.tf_p_start, + self.spec.tf_p_end, + epoch, + self.spec.epochs, + ) + ) + ar_inputs = _assemble_stage2_ar_inputs_scheduled( + self.model, + cond_cont, + cond_cat, + stage1_ctx, + sec_cont, + sec_type_idx, + n_sec, + self.particle_type_cfg, + self.model.cond_enc, + self.particle_type_emb_dim, + p_tf, + self.spec.ar_sample_steps, + ) + x1_s2 = _assemble_stage2_ar_target( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.generator, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + elif self.is_stage2: + x1_s2 = _assemble_stage2_real( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + self.generator, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + + l_gen = self._generator_loss( + cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs + ) + n_sec_logits = self._predict_n_sec(cond_cont, cond_cat, stage1_ctx) + l_nsec = torch.zeros((), device=device) + nsec_acc = torch.zeros((), device=device) + if n_sec_logits is not None: + l_nsec = F.cross_entropy(n_sec_logits, n_sec) + nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + + l_type, type_acc = self._type_loss( + cond_cont, + cond_cat, + stage1_ctx, + sec_type_idx, + sec_mask, + device, + ar_inputs=ar_inputs, + ) + + l_balance = l_proc = l_entropy = torch.zeros((), device=device) + if self.router is not None: + l_balance = self.router.balance_loss(cond_cont, cond_cat) + l_proc = self.router.classify_loss(cond_cont, cond_cat, proc_idx) + l_entropy = self.router.entropy_loss(cond_cont, cond_cat) + + total = ( + self.spec.lambda_weight * l_gen + + self.spec.n_sec_lambda * l_nsec + + self.particle_type_lambda * l_type + ) + if self.spec.lambda_balance > 0: + total = total + self.spec.lambda_balance * l_balance + if self.spec.lambda_proc > 0: + total = total + self.spec.lambda_proc * l_proc + if self.spec.lambda_entropy > 0: + total = total + self.spec.lambda_entropy * l_entropy + + return { + "loss": total, + "loss_gen": l_gen, + "loss_nsec": l_nsec, + "loss_type": l_type, + "type_acc": type_acc, + "loss_balance": l_balance, + "loss_proc": l_proc, + "loss_entropy": l_entropy, + "nsec_acc": nsec_acc, + } + + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + if self.router is not None: + self.router.gumbel_tau = _gumbel_tau( + global_step, + self.total_steps, + self.spec.gumbel_tau_start, + self.spec.gumbel_tau_end, + ) + epoch = global_step // self.spec.steps_per_epoch + out = self._compute(batch, device, epoch=epoch) + self.optimizer.zero_grad() + out["loss"].backward() + grad_norm = torch.nn.utils.clip_grad_norm_(self.params, 1.0) + self.optimizer.step() + self.lr_sched.step() + if self.ema_model is not None: + _update_ema(self.ema_model, self.model, self.ema_decay) + stats = {key: value.item() for key, value in out.items()} + stats["grad_norm"] = grad_norm.item() + stats["lr"] = self.optimizer.param_groups[0]["lr"] + return stats + + @torch.no_grad() + def val_loss(self, batch: tuple, device: torch.device) -> dict: + return { + key: value.item() for key, value in self._compute(batch, device).items() + } + + # --- reporting ------------------------------------------------------ + + def batch_loss(self, stats: dict) -> float: + return stats["loss"] + + def summary(self, means: dict) -> str: + return f"{self.name}[loss={means.get('loss', 0.0):.3f}]" + + def val_objective( + self, train_means: dict, val_means: dict, marginal_kl: float + ) -> float: + return val_means.get("loss", 0.0) + + +class WGANStageTrainer(StageTrainer): + """WGAN-GP generator+critic for a single stage (see giant/model/wgan.py). + + Ports `_wgan_train_step` to operate on one stage instead of two fused + together — the critic updates every batch; every `n_critic`-th batch + additionally updates the generator (`did_g_step`). The (non-adversarial) + n_sec classifier, when this stage's model owns it, updates every batch + regardless — folded into whichever generator optimizer step happens this + batch, same precedent as v0.2. + """ + + supports_val_loss = False + + def __init__( + self, + spec: StageSpec, + model: torch.nn.Module, + critic: torch.nn.Module, + device: torch.device, + ) -> None: + self.critic = critic.to(device) + super().__init__(spec, model, device, extra_modules=(self.critic,)) + self.n_critic = max(spec.n_critic, 1) + self.gp_weight = spec.gp_weight + self.critic_lr = spec.critic_lr + + self.g_params = list(self.model.parameters()) + self.d_params = list(self.critic.parameters()) + # WGAN-GP recipe (Gulrajani et al. 2017): Adam, beta1=0, no weight decay. + self.optimizer = optim.Adam(self.g_params, lr=spec.lr, betas=(0.0, 0.9)) + self.optimizer_d = optim.Adam( + self.d_params, + lr=spec.critic_lr if spec.critic_lr > 0 else spec.lr, + betas=(0.0, 0.9), + ) + + # Generator steps fire every n_critic-th batch, so warmup/decay must + # be counted in those units, matching v0.2. + gen_steps_per_epoch = max(spec.steps_per_epoch // self.n_critic, 1) + self._init_lr_schedule( + self.optimizer, + warmup_steps=spec.warmup_epochs * gen_steps_per_epoch, + total_steps=max(spec.epochs * gen_steps_per_epoch, 1), + ) + + train_keys = [ + "d_loss", + "g_loss", + "wasserstein", + "gp_loss", + "loss_nsec", + "nsec_acc", + "grad_norm_d", + "grad_norm_g", + ] + if self.is_stage2 and self.particle_type_cfg.get("target") == "onehot": + # §11.4 differentiability instrumentation — only meaningful when + # the type slice is a straight-through Gumbel relaxation. + train_keys += ["grad_norm_type_slice", "grad_norm_cont_slice"] + self.train_metrics = [train_metric(key) for key in train_keys] + self.val_metrics = [] + self.stage_metrics = [stage_metric("lr"), stage_metric("critic_lr")] + + def _stage2_real_and_fake(self, batch_tensors, stage1_ctx, global_step, device): + """Build `(real, fake_raw, mask, critic_fn)` for stage 2, covering + both decoders and all three particle-type targets. `fake_raw` still + needs the caller's straight-through relaxation under + `particle_type.target = "onehot"`, and neither tensor is masked-and- + multiplied on the fake side yet.""" + cond_cont, cond_cat, n_sec, sec_cont, sec_type_idx = batch_tensors + B = cond_cont.size(0) + type_dim = stage2_type_dim(self.particle_type_cfg, self.particle_type_emb_dim) + slot_width = CONT_SLOT_DIM + type_dim + k_max = sec_cont.size(1) + + sec_mask = torch.arange(k_max, device=device).unsqueeze(0) < n_sec.unsqueeze(1) + mask = sec_mask.unsqueeze(-1).expand(-1, -1, slot_width).reshape(B, -1).float() + + def critic_fn(x): + return self.critic(x, cond_cont, cond_cat, stage1_ctx) + + if self.decoder == "autoregressive": + epoch = global_step // self.spec.steps_per_epoch + p_tf = _stage2_tf_prob( + self.spec.teacher_forcing, + self.spec.tf_p_start, + self.spec.tf_p_end, + epoch, + self.spec.epochs, + ) + ar = _assemble_stage2_ar_inputs_scheduled( + self.model, + cond_cont, + cond_cat, + stage1_ctx, + sec_cont, + sec_type_idx, + n_sec, + self.particle_type_cfg, + self.model.cond_enc, + self.particle_type_emb_dim, + p_tf, + self.spec.ar_sample_steps, + ) + real = ( + _assemble_stage2_ar_target( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + "wgan", + self.model.cond_enc, + self.particle_type_emb_dim, + ).reshape(B, -1) + * mask + ) + z = torch.randn(B, k_max, self.model.noise_dim, device=device) + fake_raw = self.model( + z, + cond_cont, + cond_cat, + stage1_ctx, + ar["history_feat"], + ar["has_prev"], + ar["remaining_frac"], + ar["slot_idx"], + ).reshape(B, -1) + else: + real = ( + _assemble_stage2_real( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + "wgan", + self.model.cond_enc, + self.particle_type_emb_dim, + ) + * mask + ) + z = torch.randn(B, self.model.noise_dim, device=device) + fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) + + return real, fake_raw, mask, critic_fn + + def step(self, batch: tuple, device: torch.device, global_step: int) -> dict: + ( + cond_cont, + cond_cat, + x1_s1, + n_sec, + sec_cont, + _proc_idx, + sec_type_idx, + ) = _batch_to_device(batch, device) + B = cond_cont.size(0) + stage1_ctx = x1_s1.detach() + grad_probe: dict[str, float] = {} + + if not self.is_stage2: + real = x1_s1 + + def critic_fn(x): + return self.critic(x, cond_cont, cond_cat) + + z = torch.randn(B, self.model.noise_dim, device=device) + fake = self.model(z, cond_cont, cond_cat) + mask = None + else: + real, fake_raw, mask, critic_fn = self._stage2_real_and_fake( + (cond_cont, cond_cat, n_sec, sec_cont, sec_type_idx), + stage1_ctx, + global_step, + device, + ) + if self.particle_type_cfg.get("target", "physical") == "onehot": + # Straight-through Gumbel-softmax relaxation of the type + # slice only (decision 5) — the critic must see a hard + # one-hot forward (matching what "real" data looks like) + # while gradient still flows smoothly to the generator. + # grad_probe captures the §11.4 gradient-magnitude + # instrumentation — see _relax_onehot_type_slice's docstring. + tau = _gumbel_tau( + global_step, + self.total_steps, + self.spec.type_gumbel_tau_start, + self.spec.type_gumbel_tau_end, + ) + fake_raw = _relax_onehot_type_slice( + fake_raw, + sec_cont.size(1), + CONT_SLOT_DIM, + stage2_type_dim(self.particle_type_cfg, self.particle_type_emb_dim), + tau, + grad_probe=grad_probe, + ) + fake = fake_raw * mask + + # --- critic step (every batch) --- + fake_detached = fake.detach() + real_score = critic_fn(real) + fake_score = critic_fn(fake_detached) + gp = gradient_penalty(critic_fn, real, fake_detached, mask=mask) + d_loss = fake_score.mean() - real_score.mean() + self.gp_weight * gp + wasserstein = (real_score.mean() - fake_score.mean()).detach() + + self.optimizer_d.zero_grad() + d_loss.backward() + grad_norm_d = torch.nn.utils.clip_grad_norm_(self.d_params, 1.0) + self.optimizer_d.step() + + # --- generator (+ n_sec) step --- + did_g_step = global_step % self.n_critic == 0 + n_sec_logits = None + if self.model.n_sec_head is not None: + n_sec_logits = ( + self.model.predict_n_sec(cond_cont, cond_cat) + if not self.is_stage2 + else self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) + ) + l_nsec = torch.zeros((), device=device) + nsec_acc = torch.zeros((), device=device) + if n_sec_logits is not None: + l_nsec = F.cross_entropy(n_sec_logits, n_sec) + nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + + # On a non-generator-step batch with no n_sec_head on this stage + # (n_sec now defaults to stage 2, decision 1), there's nothing for + # the generator optimizer to do this batch — g_loss would otherwise + # be a graph-less zero tensor, which .backward() rejects outright. + skip_g_step = not did_g_step and n_sec_logits is None + if did_g_step: + g_loss_adv = generator_loss(critic_fn, fake) + g_loss = ( + self.spec.lambda_weight * g_loss_adv + self.spec.n_sec_lambda * l_nsec + ) + else: + g_loss_adv = torch.zeros((), device=device) + g_loss = self.spec.n_sec_lambda * l_nsec + if skip_g_step: + grad_norm_g = torch.zeros(()) + else: + self.optimizer.zero_grad() + g_loss.backward() + grad_norm_g = torch.nn.utils.clip_grad_norm_(self.g_params, 1.0) + self.optimizer.step() + + if did_g_step: + self.lr_sched.step() + if self.ema_model is not None: + _update_ema(self.ema_model, self.model, self.ema_decay) + + return { + "d_loss": d_loss.item(), + "g_loss": g_loss_adv.item(), + "wasserstein": wasserstein.item(), + "gp_loss": gp.item(), + "loss_nsec": l_nsec.item(), + "nsec_acc": nsec_acc.item(), + "did_g_step": did_g_step, + "grad_norm": grad_norm_d.item() + grad_norm_g.item(), + "grad_norm_d": grad_norm_d.item(), + "grad_norm_g": grad_norm_g.item(), + "grad_norm_type_slice": grad_probe.get("type", 0.0), + "grad_norm_cont_slice": grad_probe.get("cont", 0.0), + "lr": self.optimizer.param_groups[0]["lr"], + "critic_lr": self.optimizer_d.param_groups[0]["lr"], + } + + # --- reporting ------------------------------------------------------ + + def batch_loss(self, stats: dict) -> float: + return stats["d_loss"] + stats["g_loss"] + + def summary(self, means: dict) -> str: + return ( + f"{self.name}[d={means.get('d_loss', 0.0):.3f} " + f"g={means.get('g_loss', 0.0):.3f}]" + ) + + def val_objective( + self, train_means: dict, val_means: dict, marginal_kl: float + ) -> float: + """No monotone per-batch WGAN loss fit for averaging, so + best-checkpoint selection uses the real marginal-KL signal when + `validate_marginals` produced one, and falls back to this epoch's own + Wasserstein-distance magnitude otherwise. + + Behavior change vs. every run up to v0.3.0: the pre-refactor code + meant to do exactly this, but its guard + (`{n: kl for n in wgan_names if n not in val_loss_per_stage}`) could + never fire — `val_loss_per_stage` was pre-seeded with `0.0` for every + stage, so a WGAN stage contributed a flat `0.0` and the marginal KL + was recorded in the metrics row without ever influencing `best.pt`. + Runs from before this commit therefore selected their best checkpoint + on the non-adversarial stages alone.""" + if math.isfinite(marginal_kl): + return marginal_kl + return abs(train_means.get("wasserstein", 0.0)) + + # --- state ---------------------------------------------------------- + + def _extra_state(self) -> dict: + return { + "critic": self.critic.state_dict(), + "optimizer_d": self.optimizer_d.state_dict(), + } + + def _load_extra_state(self, sd: dict) -> None: + self.critic.load_state_dict(sd["critic"]) + self.optimizer_d.load_state_dict(sd["optimizer_d"]) + + def _resume_extra_lr(self, lr: float) -> None: + resumed_critic_lr = self.critic_lr if self.critic_lr > 0 else lr + for group in self.optimizer_d.param_groups: + group["lr"] = resumed_critic_lr + + +def build_stage_trainers( + cfg: dict, + models: dict[str, torch.nn.Module | None], + critics: dict[str, torch.nn.Module | None], + device: torch.device, + total_train_batches: int, +) -> dict[str, StageTrainer]: + """One trainer per active stage — `models[name] is None` means that stage + is `active = false` and is simply never constructed.""" + trainers: dict[str, StageTrainer] = {} + for name, is_stage2 in (("stage1", False), ("stage2", True)): + model = models.get(name) + if model is None: + continue + spec = StageSpec.from_config(cfg, name, is_stage2, max(total_train_batches, 1)) + if spec.generator == "wgan": + critic = critics.get(name) + assert critic is not None, ( + f"{name}_model.generator='wgan' requires a critic (see " + "giant.model.network.build_critics)" + ) + trainers[name] = WGANStageTrainer(spec, model, critic, device) + else: + trainers[name] = FlowDDPMStageTrainer(spec, model, device) + return trainers diff --git a/scripts/hparam_scan.py b/scripts/hparam_scan.py index dfb5ca2..00efd42 100644 --- a/scripts/hparam_scan.py +++ b/scripts/hparam_scan.py @@ -68,8 +68,8 @@ def final_metrics(metrics_path: Path) -> tuple[int, float, float]: with open(metrics_path, newline="") as f: rows = list(csv.DictReader(f)) epochs_completed = int(rows[-1]["epoch"]) - final_val_loss = float(rows[-1]["val_loss"]) - best_val_loss = min(float(r["val_loss"]) for r in rows) + final_val_loss = float(rows[-1]["val/loss"]) + best_val_loss = min(float(r["val/loss"]) for r in rows) return epochs_completed, final_val_loss, best_val_loss diff --git a/tests/test_train.py b/tests/test_train.py index 3e37769..992adb8 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -1,4 +1,4 @@ -"""Tests for giant/train.py.""" +"""Tests for giant/training/.""" import copy import csv @@ -18,14 +18,19 @@ from giant.constants import ( X_DIM, ) from giant.model.network import build_critics, build_models -from giant.train import ( +from giant.training import ( FlowDDPMStageTrainer, + StageSpec, WGANStageTrainer, + build_stage_trainers, + train, +) +from giant.training.metrics import _wandb_run_config +from giant.training.stage2_inputs import ( _ar_has_prev, _assemble_stage2_ar_inputs, _assemble_stage2_ar_target, _assemble_stage2_real, - _build_stage_trainers, _gumbel_tau, _relax_onehot_type_slice, _remaining_energy_fraction, @@ -33,8 +38,6 @@ from giant.train import ( _stage2_tf_prob, _stick_fraction, _type_repr, - _wandb_run_config, - train, ) PDG_VOCAB = 6 @@ -558,9 +561,9 @@ def test_metrics_csv_columns_are_stage_prefixed(): out_dir = Path(tmp) / "run" _run_train(cfg, out_dir) header = (out_dir / "metrics.csv").read_text().splitlines()[0].split(",") - assert "stage1_train_loss" in header - assert "stage2_train_d_loss" in header - assert "val_loss" in header + assert "stage1/train/loss" in header + assert "stage2/train/d_loss" in header + assert "val/loss" in header assert "epoch" in header @@ -574,22 +577,16 @@ def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): models = build_models(model_config) critics = build_critics(model_config) assert models["stage1"] is not None and critics["stage1"] is not None - trainer = WGANStageTrainer( + spec = StageSpec( name="stage1", - model=models["stage1"], - critic=critics["stage1"], is_stage2=False, - lambda_weight=1.0, - n_sec_lambda=0.1, + generator="wgan", n_critic=1000, # never a generator step in this test - gp_weight=10.0, - lr=3e-4, - critic_lr=0.0, ema_decay=0.0, - warmup_epochs=0, - epochs=1, steps_per_epoch=4, - device=torch.device("cpu"), + ) + trainer = WGANStageTrainer( + spec, models["stage1"], critics["stage1"], torch.device("cpu") ) assert trainer.model.n_sec_head is None batch = _fake_batches(1, 8)[0] @@ -598,28 +595,11 @@ def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): + spec = StageSpec( + name="stage2", is_stage2=True, generator="ddpm", ddpm_n_steps=50, ema_decay=0.0 + ) with pytest.raises(NotImplementedError): - FlowDDPMStageTrainer( - name="stage2", - model=torch.nn.Linear(1, 1), - is_stage2=True, - generator="ddpm", - lambda_weight=1.0, - n_sec_lambda=0.1, - lambda_balance=0.0, - lambda_proc=0.0, - lambda_entropy=0.0, - gumbel_tau_start=1.0, - gumbel_tau_end=0.1, - lr=3e-4, - weight_decay=0.01, - ema_decay=0.0, - warmup_epochs=0, - epochs=1, - steps_per_epoch=1, - ddpm_n_steps=50, - device=torch.device("cpu"), - ) + FlowDDPMStageTrainer(spec, torch.nn.Linear(1, 1), torch.device("cpu")) # --- AR trainer wiring (v0.3.0 step 5) -------------------------------------- @@ -649,7 +629,7 @@ def test_build_stage_trainers_ar_scheduled_and_attention_step_runs( model_config = _model_config(cfg) models = build_models(model_config) critics = build_critics(model_config) - trainers = _build_stage_trainers( + trainers = build_stage_trainers( cfg, models, critics, torch.device("cpu"), total_train_batches=4 ) trainer = trainers["stage2"] @@ -686,7 +666,7 @@ def test_train_end_to_end_ar_attention_history_scheduled_teacher_forcing( rows = list(csv.DictReader(f)) assert len(rows) == cfg["train"]["epochs"] loss_col = ( - "stage2_train_g_loss" if stage2_generator == "wgan" else "stage2_train_loss" + "stage2/train/g_loss" if stage2_generator == "wgan" else "stage2/train/loss" ) assert all(math.isfinite(float(r[loss_col])) for r in rows) @@ -705,10 +685,10 @@ def test_ar_wgan_onehot_grad_norm_instrumentation_populates_metrics(): _run_train(cfg, out_dir) with open(out_dir / "metrics.csv", newline="") as f: rows = list(csv.DictReader(f)) - assert "stage2_train_grad_norm_type_slice" in rows[0] - assert "stage2_train_grad_norm_cont_slice" in rows[0] - assert any(float(r["stage2_train_grad_norm_type_slice"]) > 0 for r in rows) - assert any(float(r["stage2_train_grad_norm_cont_slice"]) > 0 for r in rows) + assert "stage2/train/grad_norm_type_slice" in rows[0] + assert "stage2/train/grad_norm_cont_slice" in rows[0] + assert any(float(r["stage2/train/grad_norm_type_slice"]) > 0 for r in rows) + assert any(float(r["stage2/train/grad_norm_cont_slice"]) > 0 for r in rows) def test_wgan_onehot_one_shot_also_gets_grad_norm_instrumentation(): @@ -721,8 +701,8 @@ def test_wgan_onehot_one_shot_also_gets_grad_norm_instrumentation(): _run_train(cfg, out_dir) with open(out_dir / "metrics.csv", newline="") as f: rows = list(csv.DictReader(f)) - assert any(float(r["stage2_train_grad_norm_type_slice"]) > 0 for r in rows) - assert any(float(r["stage2_train_grad_norm_cont_slice"]) > 0 for r in rows) + assert any(float(r["stage2/train/grad_norm_type_slice"]) > 0 for r in rows) + assert any(float(r["stage2/train/grad_norm_cont_slice"]) > 0 for r in rows) def test_wgan_physical_omits_grad_norm_slice_columns(): @@ -731,5 +711,5 @@ def test_wgan_physical_omits_grad_norm_slice_columns(): out_dir = Path(tmp) / "run" _run_train(cfg, out_dir) header = (out_dir / "metrics.csv").read_text().splitlines()[0].split(",") - assert "stage2_train_grad_norm_type_slice" not in header - assert "stage2_train_grad_norm_cont_slice" not in header + assert "stage2/train/grad_norm_type_slice" not in header + assert "stage2/train/grad_norm_cont_slice" not in header -- 2.39.5 From d3271bc79897ae7cfdacf6472e1466436923ae41 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 09:48:58 +0200 Subject: [PATCH 13/30] Silence the fork-safety warning from num_workers>0 pipeline tests The two DataLoader-num_workers quota tests are the only ones in the file that leave num_workers>0, so they're the only ones that actually spawn forked worker subprocesses under pytest's multi-threaded process and hit Python's fork-safety DeprecationWarning. The thing under test is just the pre-flight quota-check message, emitted before the DataLoader is built. Co-Authored-By: Claude Sonnet 5 --- tests/test_pipeline.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index f85beca..f202b9d 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -186,14 +186,19 @@ def test_run_train_job_no_topn_map_for_physical_target(tmp_path, data): assert loaded.topn_maps == {} +@pytest.mark.filterwarnings("ignore::DeprecationWarning:multiprocessing.popen_fork") def test_run_train_job_warns_when_num_workers_exceeds_shared_quota( tmp_path, data, monkeypatch ): + # num_workers>0 makes DataLoader actually fork worker subprocesses + # (unlike every other test here, which runs with num_workers=0) — pytest + # itself is multi-threaded, hence Python's fork-safety warning below. monkeypatch.setattr("giant.pipeline.os.cpu_count", lambda: 8) # quota = 2 echo = _run(data, tmp_path / "out", num_workers=3) assert any("num-workers=3" in m and "exceeds" in m for m in echo) +@pytest.mark.filterwarnings("ignore::DeprecationWarning:multiprocessing.popen_fork") def test_run_train_job_no_warning_when_num_workers_within_shared_quota( tmp_path, data, monkeypatch ): -- 2.39.5 From fff61ebd612588f74b5fce8adedade701fa7ae56 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 10:32:26 +0200 Subject: [PATCH 14/30] Deduplicate giant/training/trainers.py shared per-stage logic MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lift repeated per-batch operations into StageTrainer base-class helpers so each is written once instead of being copy-pasted between FlowDDPMStageTrainer and WGANStageTrainer: - _n_sec_loss: the multiplicity classifier (stage1/stage2 predict_n_sec split + cross-entropy + accuracy), previously written three times. Gated on n_sec_head presence, not n_sec.mode, so a future stop_token model trains its EOS signal elsewhere and this stays zero. - _sec_mask: the arange < n_sec prefix mask, previously in two places. - _step_optimizer: the zero_grad/backward/clip_grad_norm_(1.0)/step quad, previously written three times; now the single home of the clip constant. - _sec_target: collapses the byte-identical _ar_target/_real wrappers into one flatten-parameterized method (they differed only by .flatten(1)). Also trim StageSpec.from_config to read DEFAULT_CONFIG-guaranteed train.* keys directly instead of re-defaulting them. The three particle-type targets (onehot CE, physical/embedding regression) and _type_loss are intentionally left as separate paths — genuinely different objectives, not duplication. stage2_inputs.py: extract the shared _ar_meta helper for the has_prev/ remaining_frac/slot_idx trio used by both AR-input assemblers. Behavior-preserving: same losses, optimizer order, and RNG draw order. Full test suite (699) green; ruff + ty clean. Co-Authored-By: Claude Opus 4.8 --- giant/training/stage2_inputs.py | 35 ++-- giant/training/trainers.py | 292 +++++++++++++++++--------------- 2 files changed, 181 insertions(+), 146 deletions(-) diff --git a/giant/training/stage2_inputs.py b/giant/training/stage2_inputs.py index 58f8908..2508f5d 100644 --- a/giant/training/stage2_inputs.py +++ b/giant/training/stage2_inputs.py @@ -138,6 +138,25 @@ def _ar_has_prev(k_max: int, device: torch.device) -> torch.Tensor: return (torch.arange(k_max, device=device) >= 1).unsqueeze(0) +def _ar_meta( + k_max: int, batch: int, device: torch.device, fraction: torch.Tensor +) -> dict[str, torch.Tensor]: + """`has_prev`/`remaining_frac`/`slot_idx` — the three per-token AR + conditioning tensors that don't depend on *which* history representation + (ground truth vs. the scheduled-sampling mix) produced `fraction`. + Shared by `_assemble_stage2_ar_inputs` and + `_assemble_stage2_ar_inputs_scheduled`, which differ only in + `history_feat`.""" + slot_idx = ( + torch.arange(k_max, device=device).float() / max(k_max - 1, 1) + ).unsqueeze(0) + return { + "has_prev": _ar_has_prev(k_max, device).expand(batch, -1), + "remaining_frac": _remaining_energy_fraction(fraction), + "slot_idx": slot_idx.expand(batch, -1), + } + + def _assemble_stage2_ar_inputs( sec_cont: torch.Tensor, sec_type_idx: torch.Tensor, @@ -161,13 +180,7 @@ def _assemble_stage2_ar_inputs( ], dim=-1, ) - slot_idx = (torch.arange(K, device=device).float() / max(K - 1, 1)).unsqueeze(0) - return { - "history_feat": history_feat, - "has_prev": _ar_has_prev(K, device).expand(B, -1), - "remaining_frac": _remaining_energy_fraction(fraction), - "slot_idx": slot_idx.expand(B, -1), - } + return {"history_feat": history_feat, **_ar_meta(K, B, device, fraction)} def _stage2_tf_prob( @@ -259,7 +272,7 @@ def _assemble_stage2_ar_inputs_scheduled( model.train() fraction_gt = _stick_fraction(sec_cont) - dir_gt = sec_cont[..., 1:4] + dir_gt = sec_cont[..., 1:CONT_SLOT_DIM] type_repr_gt = _type_repr( sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim ) @@ -275,11 +288,7 @@ def _assemble_stage2_ar_inputs_scheduled( own_feat = torch.cat([fraction.unsqueeze(-1), direction, type_repr], dim=-1) return { "history_feat": _shift_prev(own_feat), - "has_prev": _ar_has_prev(K, device).expand(B, -1), - "remaining_frac": _remaining_energy_fraction(fraction), - "slot_idx": (torch.arange(K, device=device).float() / max(K - 1, 1)) - .unsqueeze(0) - .expand(B, -1), + **_ar_meta(K, B, device, fraction), } diff --git a/giant/training/trainers.py b/giant/training/trainers.py index 5bfc78f..bc597dd 100644 --- a/giant/training/trainers.py +++ b/giant/training/trainers.py @@ -34,7 +34,6 @@ from giant.training.metrics import MetricSpec, stage_metric, train_metric, val_m from giant.training.stage2_inputs import ( _assemble_stage2_ar_inputs_scheduled, _assemble_stage2_ar_target, - _assemble_stage2_real, _gumbel_tau, _relax_onehot_type_slice, _stage2_tf_prob, @@ -148,9 +147,12 @@ class StageSpec: particle_type=cfg["stage2_model"].get("particle_type") or {"target": "physical"}, particle_type_emb_dim=cfg["conditioning"]["particle"]["emb_dim"], + # train.* keys are all guaranteed by DEFAULT_CONFIG's deep-merge + # (giant/config.py), so they read directly; the field defaults + # below exist only for tests that construct StageSpec by hand. lr=t["lr"], - weight_decay=t.get("weight_decay", 0.01), - ema_decay=t.get("ema_decay", 0.9999), + weight_decay=t["weight_decay"], + ema_decay=t["ema_decay"], warmup_epochs=t["warmup_epochs"], epochs=t["epochs"], steps_per_epoch=max(steps_per_epoch, 1), @@ -166,7 +168,7 @@ class StageSpec: # train.validate_steps as its flow-matching ODE step count — no # dedicated config key for this (docs/v0.3.0-design.md §3.3 lists # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). - ar_sample_steps=t.get("validate_steps", 10), + ar_sample_steps=t["validate_steps"], ddpm_n_steps=stage_cfg.get("ddpm", {}).get("n_steps", 1000), n_critic=wgan_cfg.get("n_critic", 5), gp_weight=wgan_cfg.get("gp_weight", 10.0), @@ -288,6 +290,125 @@ class StageTrainer: for module in self._modules: module.eval() + # --- stage-2 secondary assembly (shared by both trainer subclasses) --- + + def _ar_inputs( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_ctx: torch.Tensor, + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + n_sec: torch.Tensor, + epoch: int | None, + ) -> dict[str, torch.Tensor]: + """Per-token AR conditioning for this stage's secondary decoder. + + `epoch=None` means full teacher forcing (`p_tf=1.0`) regardless of + `spec.teacher_forcing` — the val-loss convention, kept in this one + place so both trainer subclasses honor it identically. + """ + p_tf = ( + 1.0 + if epoch is None + else _stage2_tf_prob( + self.spec.teacher_forcing, + self.spec.tf_p_start, + self.spec.tf_p_end, + epoch, + self.spec.epochs, + ) + ) + return _assemble_stage2_ar_inputs_scheduled( + self.model, + cond_cont, + cond_cat, + stage1_ctx, + sec_cont, + sec_type_idx, + n_sec, + self.particle_type_cfg, + self.model.cond_enc, + self.particle_type_emb_dim, + p_tf, + self.spec.ar_sample_steps, + ) + + def _sec_target( + self, + sec_cont: torch.Tensor, + sec_type_idx: torch.Tensor, + generator: str, + *, + flatten: bool, + ) -> torch.Tensor: + """Ground-truth stage-2 target for this stage's secondary decoder, + per the (particle-type target, generator) width rules in + `_assemble_stage2_ar_target`. `flatten=True` gives `Stage2OneShot`'s + flattened `(B, K*token_dim)` form (the old `_real`); `flatten=False` + gives `Stage2Autoregressive`'s per-token `(B, K, token_dim)` form (the + old `_ar_target`) — the two are the same tensor modulo `.flatten(1)`, + so the width rules live in one place (`stage2_inputs.py`).""" + target = _assemble_stage2_ar_target( + sec_cont, + sec_type_idx, + self.particle_type_cfg, + generator, + self.model.cond_enc, + self.particle_type_emb_dim, + ) + return target.flatten(1) if flatten else target + + @staticmethod + def _sec_mask( + n_sec: torch.Tensor, k_max: int, device: torch.device + ) -> torch.Tensor: + """`(B, K_MAX)` bool prefix mask: slot k is valid iff `k < n_sec`.""" + return torch.arange(k_max, device=device).unsqueeze(0) < n_sec.unsqueeze(1) + + def _n_sec_loss( + self, + cond_cont: torch.Tensor, + cond_cat: torch.Tensor, + stage1_ctx: torch.Tensor, + n_sec: torch.Tensor, + device: torch.device, + ) -> tuple[torch.Tensor, torch.Tensor]: + """`(l_nsec, nsec_acc)` for this stage's multiplicity classifier — + zeros when the stage owns no `n_sec_head` (stage 1 now that n_sec + defaults to stage 2, or any stage without the head). Owns the only + stage1-vs-stage2 `predict_n_sec` signature split, shared by the + non-adversarial and WGAN trainers. + + Gated on `n_sec_head is None`, not on `n_sec.mode`: a future + `mode="stop_token"` model (design doc §11.2, currently rejected in + `validate_config`) carries no head and would train its EOS signal in + the generator/AR loss path instead, so this correctly stays zero. + """ + if self.model.n_sec_head is None: + zero = torch.zeros((), device=device) + return zero, zero + logits = ( + self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) + if self.is_stage2 + else self.model.predict_n_sec(cond_cont, cond_cat) + ) + l_nsec = F.cross_entropy(logits, n_sec) + nsec_acc = (logits.argmax(dim=-1) == n_sec).float().mean() + return l_nsec, nsec_acc + + @staticmethod + def _step_optimizer( + optimizer: optim.Optimizer, loss: torch.Tensor, params: list + ) -> float: + """`zero_grad -> backward -> clip_grad_norm_(1.0) -> step`, returning + the pre-clip grad norm. The one place the grad-clip constant lives.""" + optimizer.zero_grad() + loss.backward() + grad_norm = torch.nn.utils.clip_grad_norm_(params, 1.0) + optimizer.step() + return grad_norm.item() + def _extra_state(self) -> dict: """Subclass state beyond model/optimizer/lr_sched/EMA.""" return {} @@ -342,11 +463,11 @@ class FlowDDPMStageTrainer(StageTrainer): ) super().__init__(spec, model, device) self.particle_type_lambda = self.particle_type_cfg.get("lambda", 1.0) - # Width of the type slice actually folded into x1_s2 by - # _assemble_stage2_real, under this trainer's generator (flow/ddpm - # only — see the NotImplementedError above): "physical" keeps it - # folded in (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/ - # "embedding" pull it out into model.type_head instead (0 here). + # Width of the type slice actually folded into x1_s2 by _sec_target, + # under this trainer's generator (flow/ddpm only — see the + # NotImplementedError above): "physical" keeps it folded in + # (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/"embedding" + # pull it out into model.type_head instead (0 here). self._flow_type_dim = ( None if self.particle_type_cfg.get("target", "physical") == "physical" @@ -396,13 +517,6 @@ class FlowDDPMStageTrainer(StageTrainer): ] self.stage_metrics = [stage_metric("lr")] - def _predict_n_sec(self, cond_cont, cond_cat, stage1_ctx): - if self.model.n_sec_head is None: - return None - if self.is_stage2: - return self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) - return self.model.predict_n_sec(cond_cont, cond_cat) - def _generator_loss( self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None ): @@ -501,66 +615,29 @@ class FlowDDPMStageTrainer(StageTrainer): proc_idx, sec_type_idx, ) = _batch_to_device(batch, device) - sec_mask = torch.arange(sec_cont.size(1), device=device).unsqueeze( - 0 - ) < n_sec.unsqueeze(1) + sec_mask = self._sec_mask(n_sec, sec_cont.size(1), device) stage1_ctx = x1_s1.detach() x1_s2 = None ar_inputs = None if self.is_stage2 and self.decoder == "autoregressive": - p_tf = ( - 1.0 - if epoch is None - else _stage2_tf_prob( - self.spec.teacher_forcing, - self.spec.tf_p_start, - self.spec.tf_p_end, - epoch, - self.spec.epochs, - ) + ar_inputs = self._ar_inputs( + cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch ) - ar_inputs = _assemble_stage2_ar_inputs_scheduled( - self.model, - cond_cont, - cond_cat, - stage1_ctx, - sec_cont, - sec_type_idx, - n_sec, - self.particle_type_cfg, - self.model.cond_enc, - self.particle_type_emb_dim, - p_tf, - self.spec.ar_sample_steps, - ) - x1_s2 = _assemble_stage2_ar_target( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - self.generator, - self.model.cond_enc, - self.particle_type_emb_dim, + x1_s2 = self._sec_target( + sec_cont, sec_type_idx, self.generator, flatten=False ) elif self.is_stage2: - x1_s2 = _assemble_stage2_real( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - self.generator, - self.model.cond_enc, - self.particle_type_emb_dim, + x1_s2 = self._sec_target( + sec_cont, sec_type_idx, self.generator, flatten=True ) l_gen = self._generator_loss( cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs ) - n_sec_logits = self._predict_n_sec(cond_cont, cond_cat, stage1_ctx) - l_nsec = torch.zeros((), device=device) - nsec_acc = torch.zeros((), device=device) - if n_sec_logits is not None: - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + l_nsec, nsec_acc = self._n_sec_loss( + cond_cont, cond_cat, stage1_ctx, n_sec, device + ) l_type, type_acc = self._type_loss( cond_cont, @@ -612,15 +689,12 @@ class FlowDDPMStageTrainer(StageTrainer): ) epoch = global_step // self.spec.steps_per_epoch out = self._compute(batch, device, epoch=epoch) - self.optimizer.zero_grad() - out["loss"].backward() - grad_norm = torch.nn.utils.clip_grad_norm_(self.params, 1.0) - self.optimizer.step() + grad_norm = self._step_optimizer(self.optimizer, out["loss"], self.params) self.lr_sched.step() if self.ema_model is not None: _update_ema(self.ema_model, self.model, self.ema_decay) stats = {key: value.item() for key, value in out.items()} - stats["grad_norm"] = grad_norm.item() + stats["grad_norm"] = grad_norm stats["lr"] = self.optimizer.param_groups[0]["lr"] return stats @@ -719,7 +793,7 @@ class WGANStageTrainer(StageTrainer): slot_width = CONT_SLOT_DIM + type_dim k_max = sec_cont.size(1) - sec_mask = torch.arange(k_max, device=device).unsqueeze(0) < n_sec.unsqueeze(1) + sec_mask = self._sec_mask(n_sec, k_max, device) mask = sec_mask.unsqueeze(-1).expand(-1, -1, slot_width).reshape(B, -1).float() def critic_fn(x): @@ -727,36 +801,13 @@ class WGANStageTrainer(StageTrainer): if self.decoder == "autoregressive": epoch = global_step // self.spec.steps_per_epoch - p_tf = _stage2_tf_prob( - self.spec.teacher_forcing, - self.spec.tf_p_start, - self.spec.tf_p_end, - epoch, - self.spec.epochs, - ) - ar = _assemble_stage2_ar_inputs_scheduled( - self.model, - cond_cont, - cond_cat, - stage1_ctx, - sec_cont, - sec_type_idx, - n_sec, - self.particle_type_cfg, - self.model.cond_enc, - self.particle_type_emb_dim, - p_tf, - self.spec.ar_sample_steps, + ar = self._ar_inputs( + cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch ) real = ( - _assemble_stage2_ar_target( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - "wgan", - self.model.cond_enc, - self.particle_type_emb_dim, - ).reshape(B, -1) + self._sec_target(sec_cont, sec_type_idx, "wgan", flatten=False).reshape( + B, -1 + ) * mask ) z = torch.randn(B, k_max, self.model.noise_dim, device=device) @@ -771,17 +822,7 @@ class WGANStageTrainer(StageTrainer): ar["slot_idx"], ).reshape(B, -1) else: - real = ( - _assemble_stage2_real( - sec_cont, - sec_type_idx, - self.particle_type_cfg, - "wgan", - self.model.cond_enc, - self.particle_type_emb_dim, - ) - * mask - ) + real = self._sec_target(sec_cont, sec_type_idx, "wgan", flatten=True) * mask z = torch.randn(B, self.model.noise_dim, device=device) fake_raw = self.model(z, cond_cont, cond_cat, stage1_ctx) @@ -848,31 +889,19 @@ class WGANStageTrainer(StageTrainer): d_loss = fake_score.mean() - real_score.mean() + self.gp_weight * gp wasserstein = (real_score.mean() - fake_score.mean()).detach() - self.optimizer_d.zero_grad() - d_loss.backward() - grad_norm_d = torch.nn.utils.clip_grad_norm_(self.d_params, 1.0) - self.optimizer_d.step() + grad_norm_d = self._step_optimizer(self.optimizer_d, d_loss, self.d_params) # --- generator (+ n_sec) step --- did_g_step = global_step % self.n_critic == 0 - n_sec_logits = None - if self.model.n_sec_head is not None: - n_sec_logits = ( - self.model.predict_n_sec(cond_cont, cond_cat) - if not self.is_stage2 - else self.model.predict_n_sec(cond_cont, cond_cat, stage1_ctx) - ) - l_nsec = torch.zeros((), device=device) - nsec_acc = torch.zeros((), device=device) - if n_sec_logits is not None: - l_nsec = F.cross_entropy(n_sec_logits, n_sec) - nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean() + l_nsec, nsec_acc = self._n_sec_loss( + cond_cont, cond_cat, stage1_ctx, n_sec, device + ) # On a non-generator-step batch with no n_sec_head on this stage # (n_sec now defaults to stage 2, decision 1), there's nothing for # the generator optimizer to do this batch — g_loss would otherwise # be a graph-less zero tensor, which .backward() rejects outright. - skip_g_step = not did_g_step and n_sec_logits is None + skip_g_step = not did_g_step and self.model.n_sec_head is None if did_g_step: g_loss_adv = generator_loss(critic_fn, fake) g_loss = ( @@ -882,12 +911,9 @@ class WGANStageTrainer(StageTrainer): g_loss_adv = torch.zeros((), device=device) g_loss = self.spec.n_sec_lambda * l_nsec if skip_g_step: - grad_norm_g = torch.zeros(()) + grad_norm_g = 0.0 else: - self.optimizer.zero_grad() - g_loss.backward() - grad_norm_g = torch.nn.utils.clip_grad_norm_(self.g_params, 1.0) - self.optimizer.step() + grad_norm_g = self._step_optimizer(self.optimizer, g_loss, self.g_params) if did_g_step: self.lr_sched.step() @@ -902,9 +928,9 @@ class WGANStageTrainer(StageTrainer): "loss_nsec": l_nsec.item(), "nsec_acc": nsec_acc.item(), "did_g_step": did_g_step, - "grad_norm": grad_norm_d.item() + grad_norm_g.item(), - "grad_norm_d": grad_norm_d.item(), - "grad_norm_g": grad_norm_g.item(), + "grad_norm": grad_norm_d + grad_norm_g, + "grad_norm_d": grad_norm_d, + "grad_norm_g": grad_norm_g, "grad_norm_type_slice": grad_probe.get("type", 0.0), "grad_norm_cont_slice": grad_probe.get("cont", 0.0), "lr": self.optimizer.param_groups[0]["lr"], -- 2.39.5 From 630d8d3992121b0b66d61086c716f6c341d5bd3b Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 10:49:50 +0200 Subject: [PATCH 15/30] Rewrite README for v0.3.0 architecture, quick start, and data columns MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The two-stage architecture description had drifted from the v0.3.0 stage-2-autoregressive redesign (8 commits, eb6dd27..da7cde3) — it still documented the old one-shot-only SecondaryDecoder and continuous mass/charge secondary target. Restructured for faster onboarding: a Quick start section up front, bullet-point Architecture and training-flag docs instead of dense paragraphs, and a Data section listing the actual parquet columns consumed by giant/data/loader.py. Dropped the Roadmap section (status/history, not architecture) and CLI-flag default callouts from Architecture, keeping it focused on net structure. Co-Authored-By: Claude Sonnet 5 --- README.md | 103 ++++++++++++++++++++++++++++++------------------------ 1 file changed, 57 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index 8365696..c2244f5 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,29 @@ # giant -**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. +**G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate. -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. +A conditional generative model that replaces the Geant4 step function: given a pre-step particle state it samples a post-step outcome — the primary's continuation plus its secondary particles — and autoregressively rolls that out into full showers. Trained entirely from parquet dumps of the miniCaloSim steps tree; no Geant4 runtime dependency. -Training is driven entirely from parquet files of the miniCaloSim steps tree. No Geant4 runtime dependency. +## Quick start + +```bash +uv sync --extra cpu # install deps (CPU torch; use --extra cuda for GPU) + +giant new-run --hidden-dim 512 --lr 3e-4 # scaffold config.toml + run dir +giant train path/to/steps.parquet # train (flow + wgan by default) +giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt + +dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl # needed for rollout +giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl +``` + +Every command takes `--help` for the full flag list, and `--config config.toml` for anything not exposed as a flag. ## Architecture -A **two-stage model**, both stages checkpointed together, with a choice of generative mode per stage (`--mode`): +A **two-stage model**, checkpointed together. Either stage's outcome can be produced by one of three interchangeable generative objectives (`--stage1-generator`/`--stage2-generator`, or `--mode` to set both at once): `flow` (conditional flow matching, ODE-sampled in ~10 steps), `ddpm` (denoising diffusion), or `wgan` (single-pass WGAN-GP generator/critic). -- **`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. -- **`ddpm`** — a standard denoising diffusion baseline for comparison (`giant/model/schedule.py:CosineSchedule`). -- **`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. - -**Stage 1 — primary (9D, `giant/constants.py:LOCAL_TARGET_NAMES`):** +**Stage 1 — primary step.** Predicts the 9D post-step outcome (`giant/constants.py:LOCAL_TARGET_NAMES`) from the pre-step conditioning: | Index | Variable | Encoding | |-------|----------|----------| @@ -23,36 +32,29 @@ A **two-stage model**, both stages checkpointed together, with a choice of gener | 3–5 | `post_dir` in local frame | unit vector | | 6–8 | `travel_dir` (`post_pos − pre_pos`) in local frame | unit vector | -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. +- Energy logits decode via softmax over `[edep_logit, sec_logit, 0]` × `pre_E`, so `edep + e_sec + post_E == pre_E` exactly — conservation is architectural, not learned. +- `post_dir`/`travel_dir` live in the frame where `pre_dir = ẑ`. `post_pos` isn't a target — it's reconstructed as `pre_pos + step_length * world_frame(travel_dir)`. -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. +**Stage 2 — secondaries.** Conditioned on the pre-step state and Stage 1's outcome, it generates the variable-length list of secondary particles. Two decoding strategies (`--stage2-decoder`): -**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. +- `autoregressive` — emits secondaries one at a time in descending-energy order, each token conditioned on a running history of prior tokens (`markov`: previous token only, or `attention`: causal self-attention, KV-cached at inference) +- `one_shot` — all `K_MAX` slots generated in a single forward pass, masked past the predicted `n_sec` -**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: +Either way, secondary energies stick-break the `e_sec` budget handed down from Stage 1, so the full chain conserves energy. A secondary's particle identity is represented as `onehot` (categorical, top-N PDG codes + "other"), `physical` (continuous log-mass/charge), or `embedding` (nearest-neighbour lookup). -- **`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. -- **`embedding`** — the original design: a learned `nn.Embedding` per PDG code / material, kept as a generalization-comparison baseline (memorizes the training menu). +**Conditioning.** Pre-step position/energy/direction/layer, plus particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, encoded the same three ways as particle identity above (`--conditioning`) — the `physical` representation generalizes to species/materials outside the training menu since it's computed rather than looked up. `n_sec`/`e_sec` are always model outputs, never conditioning inputs. -`n_sec` and `e_sec` are model outputs, not conditioning inputs — a rollout is self-contained and never injects ground truth. - -**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`. - -## Roadmap - -**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). - -**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. - -**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). - -**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. - -A multi-material sampling-calorimeter dataset is a planned future direction, not yet built. +**MoE routing** (`--router`, either stage): a pluggable `Router` (`energy`/`pdg`/`process`/`composed` axes) top-1-dispatches each row to one of several small expert trunks at eval time, instead of running one monolithic trunk. ## Data -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. +- Input: parquet files produced by [miniCaloSim](https://gitlab.etp.kit.edu/lbogner/minicalosim), or converted from ROOT via `dwarf convert`. One row = one Geant4 step. +- **Conditioning (pre-step) columns:** `event_id`, `pdg`, `pre_x`/`pre_y`/`pre_z`, `pre_E`, `pre_dx`/`pre_dy`/`pre_dz` (direction), `material`, `layer_id`. +- **Primary outcome (post-step) columns:** `post_x`/`post_y`/`post_z`, `post_E`, `post_dx`/`post_dy`/`post_dz`, `step_length`, `edep` (energy deposited in this step), `e_sec` (total energy carried off by secondaries), `child_track_ids` (its length gives `n_sec`). +- **Secondary columns**, one variable-length list per step: `sec_pdg_list`, `sec_E_list`, `sec_dx_list`/`sec_dy_list`/`sec_dz_list` — padded/truncated to `K_MAX` (15) slots on load, ordered by descending energy. +- **Optional:** `process` — the physics process that produced the step (e.g. `compt`, `phot`, `eBrem`); a post-step label used only as classifier supervision (`ProcessRouter`), never as conditioning. +- Train/val split is by `event_id` (`--seed`-controlled), not row shuffle, so correlated steps from the same shower never leak across the split. +- Loading a directory or `.manifest` of multiple parquet files (each one Geant4 job, `event_id` restarting from 0) offsets each file's `event_id`s by a fixed per-file stride so ids stay globally unique across files. ## Project structure @@ -64,11 +66,11 @@ giant/ │ │ ├── transforms.py # log transforms, local-frame rotation, energy simplex, secondary encode/decode │ │ └── dataset.py # StepsDataset / StreamingStepsDataset (PyTorch) │ ├── model/ -│ │ ├── network.py # ConditionEncoder, DenoisingMLP, SecondaryDecoder, Router/MoE, WGAN generator/critic +│ │ ├── network.py # ConditionEncoder, Stage1Model, Stage2OneShot/Stage2Autoregressive, Router/MoE, CriticModel │ │ ├── schedule.py # CosineSchedule (DDPM) and flow matching utilities │ │ └── wgan.py # WGAN-GP gradient penalty / critic / generator losses │ ├── constants.py # output/conditioning dims, K_MAX, secondary slot layout, schema keys -│ ├── particles.py # PDG → (mass, charge) decode, incl. nuclear/ion codes; nearest-known-PDG lookup +│ ├── particles.py # PDG → (mass, charge) decode, incl. nuclear/ion codes; onehot/embedding secondary-identity decode │ ├── materials.py # material name → (Z_eff, A_eff, density, X0, λ_int) │ ├── config.py # default hyperparameters, TOML config merging, device autodetect │ ├── pipeline.py # builds datasets/normalizers and kicks off a training run (with setup-stage caching) @@ -111,39 +113,48 @@ giant/ ## Setup ```bash -uv sync --extra cpu # CPU-only torch (use --extra cuda for CUDA 11.8 instead) -uv sync --extra cpu --extra dev # add dev tools (pytest, ruff, ty) +uv sync --extra cpu # CPU-only torch (use --extra cuda for CUDA 11.8 instead) +uv sync --extra cpu --extra dev # add dev tools (pytest, ruff, ty) uv sync --extra cpu --extra geometry # add scikit-learn, for `dwarf build-geometry-oracle` / rollout ``` -`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. +`cpu` and `cuda` are mutually exclusive — pick one to select the torch build (pinned to 2.3.x). Plain `uv sync` installs no torch at all. See `CLAUDE.md` for details. -## Training, prediction, and rollout +## Training, prediction, rollout ```bash -giant new-run --hidden-dim 512 --lr 3e-4 --comment "..." # scaffold a config.toml + run dir for a new run -giant train path/to/steps.parquet --mode flow # train (flow matching; also --mode ddpm / wgan) +giant new-run --hidden-dim 512 --lr 3e-4 --comment "..." # scaffold a config.toml + run dir +giant train path/to/steps.parquet # train (flow stage 1 + wgan stage 2, default) giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt -# Full-shower rollout needs a geometry oracle (position → material/layer_id): -dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl +dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl # position → material/layer_id giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl ``` -`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`. Metric names are `//` (e.g. `stage1/train/loss`, `stage2/train/d_loss`, `stage1/lr`), plus an unprefixed run-level tail (`val/loss`, `grad_norm`, `epoch_time_s`, ...); each is declared once on the `StageTrainer` that computes it (`giant/training/trainers.py`), so the CSV header and the W&B panel names are derived, never hand-maintained. 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. +Useful flags on `giant train`: + +- `--mode {flow,ddpm,wgan}` sets both stages' objective at once; `--stage1-generator`/`--stage2-generator` override per stage +- `--stage2-decoder {autoregressive,one_shot}` — Stage 2 decoding strategy (see Architecture) +- `--conditioning {physical,embedding,onehot}` — conditioning representation +- `--router` / `--router-type` / `--n-experts` / `--router-axis` — MoE routing +- `--wandb` — log per-epoch metrics to Weights & Biases (needs `uv sync --extra wandb`); metric names are `//` plus an unprefixed run-level tail, all derived from `giant/training/trainers.py` `MetricSpec`s +- `--no-cache-setup` / `--rebuild-setup-cache` — control the setup-stage sidecar cache (vocab maps, event split, normalizer stats); `dwarf warm-cache` precomputes it + +Config-file-only knobs (no CLI flag — use `--config config.toml`): `stage2_model.autoregressive.teacher_forcing`/`.history`, `stage2_model.particle_type.target`. v0.2 flat-schema configs and checkpoints load fine (auto-migrated). + +`giant rollout` seeds showers from each event's highest-energy entry step, then autoregressively steps the model to completion, pushing secondaries as new tracks and looking up `material`/`layer_id` from the geometry oracle each step. Tracks terminate on energy cutoff, max steps, detector escape, or natural end; energy is deposited locally on every stop except escape, so showers conserve energy by construction. ## Validation and analysis -`giant.validate.validate_marginals` runs step-level marginal and KL-divergence checks during training (`--validate-every`). - -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: +- `giant.validate.validate_marginals` — step-level marginal + KL-divergence checks during training (`--validate-every`) +- `giant analyze` — deeper rollout-vs-reference diagnostics (marginals by energy/pdg/material, per-event totals, shower profiles, species share, leakage, secondaries): ```bash giant analyze submit rollout.yaml --accounting-group cms # prep + one HTCondor job per plot (compute only) giant analyze render --gallery # local: styled PDFs + HTML gallery (needs LaTeX) ``` -`` 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. +`` is derived next to the rollout parquet (`analyze prep`/`submit` print it). Compute jobs are polars/numpy only; only `render` needs LaTeX, so it always runs locally. ## Development -- 2.39.5 From f46628141dc2be7fc96d3074dcf9f8d97e5de221 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 10:50:38 +0200 Subject: [PATCH 16/30] Bump version to 0.3.0 Co-Authored-By: Claude Sonnet 5 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e7fad25..0e0288e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "giant" -version = "0.2.0" +version = "0.3.0" description = "Geant4 step-function surrogate via conditional flow matching" readme = "README.md" requires-python = ">=3.12" -- 2.39.5 From 878e9ddca3eb41e0dac12a66fa5410a4245b83a7 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:19:02 +0200 Subject: [PATCH 17/30] Delete docs/v0.3.0-design.md and strip all references to it MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The design doc and its followups doc are no longer needed as a live reference now that the v0.3.0 redesign is implemented — comments and docstrings across the codebase cited it extensively (file path, "design doc §X.Y", "decision N", or bare "§X.Y" section numbers) as design rationale. Removed docs/ and edited every citing comment/docstring to drop the now-dangling reference while keeping the substantive explanation next to it. CLAUDE.md's v0.3.0 roadmap bullet loses its trailing pointer to the deleted file. Verified: no remaining "docs/v0.3.0", "design doc", "decision N", or "§N.N" references (repo-wide grep); ruff and ty clean; full test suite on the heaviest-touched modules (network, sample, rollout, migration, config, train) passes. Co-Authored-By: Claude Sonnet 5 --- CLAUDE.md | 2 +- docs/v0.3.0-design.md | 1002 --------------------- docs/v0.3.0-followups.md | 53 -- giant/analysis/catalog.py | 2 +- giant/analysis/condor.py | 2 +- giant/analysis/render.py | 4 +- giant/analysis/router_gating.py | 4 +- giant/analysis/type_embedding_distance.py | 2 +- giant/cli.py | 16 +- giant/config.py | 14 +- giant/data/dataset.py | 3 +- giant/data/loader.py | 11 +- giant/data/setup_cache.py | 6 +- giant/data/transforms.py | 35 +- giant/model/network.py | 97 +- giant/model/schedule.py | 6 +- giant/particles.py | 20 +- giant/pipeline.py | 20 +- giant/rollout.py | 35 +- giant/sample.py | 52 +- giant/training/loop.py | 7 +- giant/training/stage2_inputs.py | 28 +- giant/training/trainers.py | 36 +- giant/validate.py | 2 +- scripts/check_migration_v02_v03.py | 10 +- scripts/dwarf.py | 2 +- scripts/warm_setup_cache.py | 4 +- tests/legacy/network_v02_snapshot.py | 8 +- tests/test_cli_train_overrides.py | 5 +- tests/test_config.py | 8 +- tests/test_loader.py | 3 +- tests/test_migration_v02_v03.py | 28 +- tests/test_network.py | 18 +- tests/test_pipeline.py | 10 +- tests/test_rollout.py | 20 +- tests/test_router.py | 8 +- tests/test_sample.py | 6 +- tests/test_train.py | 12 +- tests/test_type_embedding_distance.py | 2 +- tests/test_validate.py | 4 +- uv.lock | 2 +- 41 files changed, 263 insertions(+), 1346 deletions(-) delete mode 100644 docs/v0.3.0-design.md delete mode 100644 docs/v0.3.0-followups.md diff --git a/CLAUDE.md b/CLAUDE.md index c3cc181..7c89dab 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -91,6 +91,6 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). -**v0.3.0 — Stage-2 autoregressive redesign (designed, not implemented; branch `v0.3.0-stage2-autoregressive`):** the 2026-08-03 WGAN rollout benchmark failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). The agreed response pivots Stage 2 to **autoregressive generation** in descending-energy order with teacher forcing, and switches the particle-type representation back to **categorical** (top N−1 by training-set count + an "other" bucket), reversing the 2026-07-17 continuous `(log-mass, charge)` target. This requires a config break: `[conditioning]` / `[stage1_model]` / `[stage2_model]` / `[train]` blocks replace the single global `train.mode` + `[model]`, so per-stage generators (`stage1 = flow` + `stage2 = wgan`), stage-2-only training, and one-shot-vs-autoregressive comparison are all expressible. `network.py` is refactored from ten permutation classes into composable parts (encoder × trunk × objective), which also makes routed WGAN work for the first time. **Full design contract, with every config option documented: `docs/v0.3.0-design.md` — read it before touching `giant/config.py` or `giant/model/network.py`.** +**v0.3.0 — Stage-2 autoregressive redesign (designed, not implemented; branch `v0.3.0-stage2-autoregressive`):** the 2026-08-03 WGAN rollout benchmark failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). The agreed response pivots Stage 2 to **autoregressive generation** in descending-energy order with teacher forcing, and switches the particle-type representation back to **categorical** (top N−1 by training-set count + an "other" bucket), reversing the 2026-07-17 continuous `(log-mass, charge)` target. This requires a config break: `[conditioning]` / `[stage1_model]` / `[stage2_model]` / `[train]` blocks replace the single global `train.mode` + `[model]`, so per-stage generators (`stage1 = flow` + `stage2 = wgan`), stage-2-only training, and one-shot-vs-autoregressive comparison are all expressible. `network.py` is refactored from ten permutation classes into composable parts (encoder × trunk × objective), which also makes routed WGAN work for the first time. **Condor-submitted GPU training/rollout (in progress, `condor-gpu-train-rollout` branch, not yet merged):** moves `giant train`/`giant rollout` off the shared portal GPU dev machines (see Compute environment) onto remote-GPU HTCondor submission on TOpAS/NEMO2 (`giant/condor.py`). Partway between "needs major features" and feature-complete — not ready to merge yet. diff --git a/docs/v0.3.0-design.md b/docs/v0.3.0-design.md deleted file mode 100644 index 81f9698..0000000 --- a/docs/v0.3.0-design.md +++ /dev/null @@ -1,1002 +0,0 @@ -# GIANT v0.3.0 — Stage-2 autoregressive redesign - -**Status:** design agreed, not implemented. Branch `v0.3.0-stage2-autoregressive`. -**Date:** 2026-08-04. -**Source:** `~/knowledge-base/meetings/2026-08-04-jan-stage2-autoregressive-architecture.md` -(meeting with Jan), plus the design decisions taken in the session that produced -this document. - -This document is the implementation contract for v0.3.0. It specifies the new -config format option by option, the `network.py` refactor, and the order in which -to build it. Read it before touching `giant/config.py` or `giant/model/network.py`. - ---- - -## Table of contents - -1. [Why](#1-why) -2. [Decisions register](#2-decisions-register) -3. [Config format reference](#3-config-format-reference) -4. [Migration: v0.2 -> v0.3](#4-migration-v02---v03) -5. [network.py refactor](#5-networkpy-refactor) -6. [Stage-2 autoregressive design](#6-stage-2-autoregressive-design) -7. [Training loop](#7-training-loop) -8. [Data and setup-cache changes](#8-data-and-setup-cache-changes) -9. [Config machinery changes](#9-config-machinery-changes) -10. [Callers that need updating](#10-callers-that-need-updating) -11. [Settled scope and open questions](#11-settled-scope-and-open-questions) -12. [Implementation order](#12-implementation-order) - ---- - -## 1. Why - -The 2026-08-03 WGAN rollout benchmark -(`~/knowledge-base/experiments/giant-wgan-physical-rollout-validation.md`) was -good at the primary-step level and **failed at the secondary-species level**: -zero photon secondaries generated, ~4M hallucinated `-14` (muon antineutrino) -secondaries — a species essentially absent from Geant4. - -Stage 1 is not implicated; the meeting scoped everything to Stage 2. Two changes -were agreed together: - -- **Autoregressive generation** over secondaries in decreasing energy order, - replacing the one-shot masked `K_MAX=15` prediction, trained with teacher - forcing. -- **Categorical particle type** with a data-derived "other" bucket, reversing the - 2026-07-17 move to a continuous `(log-mass, charge)` target. Working hypothesis: - the continuous target is part of what let the generator collapse onto degenerate - species. - -The meeting also set a **methodology**: compare Stage-2 architectures *standalone* -(trained directly on secondary columns, no Stage-1 forward pass) before chaining -the winner behind Stage 1. Iterating on the compounding-rollout-error problem is -far cheaper that way than paying for a full two-stage run per candidate. - -That methodology is what forces the config refactor: v0.2 has a single global -`train.mode` and a single `[model]` block, with no way to express "train stage 2 -only", "stage 1 flow + stage 2 WGAN", or "one-shot vs autoregressive stage 2". - ---- - -## 2. Decisions register - -| # | Decision | Rationale | -|---|----------|-----------| -| 1 | **`n_sec` head moves from stage 1 to stage 2** | Stage 1 becomes the pure 9D primary step. A stage-2-only run is then self-contained (it can predict its own multiplicity), and §3's "implicit stop token" alternative gets a natural home in the same config block. | -| 2 | **Full mixed per-stage objectives** | `stage1 = flow` + `stage2 = wgan` must actually run — Stage 1 is good as flow, Stage 2 is what is being iterated on. The train loop becomes one trainer object per stage, each owning its optimizers and update cadence. | -| 3 | **Migration shim for configs *and* checkpoints** | Nothing on `/ceph` goes dead. v0.2 `config.toml` files and v0.2 `model_config` dicts are translated on load. | -| 4 | **Nested objective sub-tables** | `[stage1_model.wgan]`, `[stage2_model.ddpm]` rather than flat `wgan_n_critic` keys — self-documenting about which keys the active generator ignores, and validation can warn on a populated sub-table that is never read. | -| 5 | **Particle type is adversarial: straight-through Gumbel into the critic** | The critic sees a relaxed one-hot alongside energy/direction, so the joint (species, kinematics) distribution is learned rather than factorized. Makes the collapse hypothesis directly testable instead of assumed. Accepts a **biased** gradient through the type path — assumed negligible, with a validation obligation in §11.4. | -| 6 | **No charge conservation in v0.3.0** | Explicitly "not yet worked out" in the meeting. No `[stage2_model.conservation]` block at all. Energy conservation stays exact and implicit in the stick-breaking encoding. | -| 7 | **Explicit stage-prefixed CLI flags** | `--stage2-hidden-dim` etc., no generic `--set path=value`. Discoverable via `--help` and tab-completable; the cost is a flag list kept in sync with `DEFAULT_CONFIG` by hand. | -| 8 | **AR history is a config axis, markov default** | Markov (previous token + remaining budget + slot index) is the baseline that makes the meeting's §6 "is attention useful" question answerable by ablation rather than by comparing differently-shaped models. Both sit behind one `history_encoder(prefix) -> vector` interface. | - -### 2.1 Consequence of decision 5 - -Straight-through Gumbel needs a critic to receive the relaxed one-hot. So the -type mechanism is **implied by the generator**, and needs no config key of its own: - -| `stage2_model.generator` | Type mechanism | -|--------------------------|----------------| -| `"wgan"` | The type slice goes into the critic's input alongside energy/direction. Adversarial; learns the joint. Under `particle_type.target = "onehot"` it is relaxed through ST-Gumbel first; `"physical"` and `"embedding"` are already continuous and feed the critic directly. | -| `"flow"` / `"ddpm"` | No critic exists -> the type slice trains against its own target, weighted by `particle_type.lambda` (same pattern as `n_sec` today): cross-entropy for `"onehot"`, regression for `"physical"` / `"embedding"`. Non-adversarial. | - -There is therefore **no `particle_type.adversarial` key** — the mechanism follows -from `generator` × `particle_type.target`. - -### 2.2 Rejected alternatives worth remembering - -- **`tie_to_stage1` as a tri-state** (`none`/`gate`/`full`). Sharing experts - between stages is impossible — the stage-1 trunk's input is the 9D target - vector, stage 2's is a token vector of a different width. Sharing the *gate* - is the only meaningful tying, so the key is a bool. -- **Generic `--set path.to.key=value` CLI overrides.** Considered and rejected in - favour of explicit flags (decision 7). -- **Per-expert trunk sizing** (`expert_hidden_dim` / `expert_n_blocks`). Removed - in v0.3.0: experts always use the stage's own `hidden_dim` / `n_res_blocks`. - This was already the effective behaviour — v0.2's `0` sentinel meant "inherit" - and nothing ever set it otherwise. Consequence worth knowing: a routed model is - `n_experts` × the parameters of the monolith at **equal per-row eval cost** - (top-1 dispatch runs one full-size trunk), so routing buys specialization, not - a per-call speedup. - -### 2.3 Correction to an earlier claim - -An earlier draft of this design asserted that a one-hot conditioning mode is -"mathematically identical to an `nn.Embedding` lookup" and should be dropped. -**That is wrong for the mode specified in §3.1.** One-hot here is a *fixed, -unlearned* vector of width `emb_dim` covering the top `emb_dim - 1` species by -training-set count plus an "other" bin. The difference from `embedding` is the -**vocabulary cap**, not the parameterization: with 237 PDG codes and -`emb_dim = 16`, `embedding` gives 237 distinct learned vectors while `onehot` -gives 16 classes. That is a real capacity difference and a real statement about -how rare species are treated, so all three modes are kept. - ---- - -## 3. Config format reference - -Six top-level blocks: `[conditioning]`, `[stage1_model]`, `[stage2_model]`, -`[train]`, plus per-stage sub-tables and the existing `[meta]` (written by -`save_config`, never hand-authored). - -### 3.1 `[conditioning]` - -One block shared by both stages. Each stage still builds its own encoder -*instance* (separate weights) unless `share_stages = true`. - -**The particle and material axes are configured independently and may mix -freely** — e.g. material `physical` with particle `embedding` is a valid and -intended combination. - -```toml -[conditioning] -out_dim = 128 -share_stages = false - -[conditioning.particle] -type = "physical" -emb_dim = 16 -n_layers = 1 - -[conditioning.material] -type = "physical" -emb_dim = 16 -n_layers = 1 -``` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `out_dim` | int | `128` | Width of the fused conditioning vector produced by the encoder's fusion MLP, consumed by every downstream trunk. **New in v0.3.0** — v0.2 hardcoded this as `cond_out_dim = 128` in every constructor signature, unreachable from config. | -| `share_stages` | bool | `false` | `false`: stage 1 and stage 2 each construct their own `ConditionEncoder` with identical config but independent weights (v0.2 behaviour). `true`: one instance, shared by reference. Shared weights halve the conditioning parameter count and force a common representation; independent weights let each stage specialize its view of the pre-step state. | - -#### `[conditioning.particle]` and `[conditioning.material]` - -Identical key sets, applied to the two identity axes independently. - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `type` | `"physical"` \| `"embedding"` \| `"onehot"` | `"physical"` | How this axis's identity becomes an `emb_dim`-wide vector. See the table below. | -| `emb_dim` | int | `16` | Width of this axis's vector. Under `"onehot"` it **also sets the class count** — see below. | -| `n_layers` | int | `1` | Depth of the sub-MLP under `"physical"`. `1` is the single-layer net with `emb_dim` neurons. Ignored under `"embedding"` / `"onehot"`. | - -**The three modes:** - -| mode | particle input | material input | width | learned parameters | -|------|----------------|----------------|-------|--------------------| -| `"physical"` | `log(mass)`, `charge` (`PARTICLE_PHYS_DIM = 2`) | `Z_eff`, `A_eff`, `log(density)`, `log(X0)`, `log(λ_int)` (`MATERIAL_PHYS_DIM = 5`) | `emb_dim` | one `n_layers`-deep MLP with `emb_dim` neurons | -| `"embedding"` | dense vocab index | dense vocab index | `emb_dim` | `nn.Embedding(vocab, emb_dim)` | -| `"onehot"` | top `emb_dim - 1` PDG codes by training-set count, plus one "other" bin | top `emb_dim - 1` materials by count, plus "other" | `emb_dim` | **none** — a fixed vector | - -- `"physical"` reads columns already present in `cond_cont[:, COND_DIM_BASE:]` - (see `giant.data.transforms.build_features`). It is computable for **any** PDG - code or material, which is what allows generalization beyond the training menu. -- `"embedding"` memorizes the training menu — the generalization-comparison - baseline, and the only mode that supports - `stage2_model.particle_type.target = "embedding"` (§3.3). -- `"onehot"` is a *fixed, unlearned* representation. It is **not** a - reparameterization of `"embedding"`: the difference is the vocabulary cap. With - 237 PDG codes and `emb_dim = 16`, `"embedding"` gives 237 distinct learned - vectors while `"onehot"` gives 16 classes. Needs the same data-derived top-N - map as the stage-2 type target (§8). - -**Interaction:** `particle.type = "physical"` is incompatible with router types -`"pdg"` and `"process"`, which build their own dataset-scoped -`nn.Embedding(pdg_vocab, ...)` regardless of the trunk's conditioning mode. -Pairing them silently reintroduces a training-menu-scoped lookup at the routing -layer, defeating the point of physical conditioning. Rejected loudly at build time -— `_check_router_conditioning_compat` in `network.py`, which carries over but must -now read `conditioning.particle.type` rather than a single global mode. - -### 3.2 `[stage1_model]` - -Stage 1 predicts the 9D primary post-step vector -(`giant/constants.py:LOCAL_TARGET_NAMES`). As of decision 1 it carries **no** -`n_sec` head. - -```toml -[stage1_model] -active = true -generator = "flow" -hidden_dim = 256 -n_res_blocks = 6 -dropout = 0.0 -lambda = 1.0 -``` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `active` | bool | `true` | `false` skips building and training stage 1 entirely. The resulting checkpoint holds only stage 2 and **cannot be rolled out** — `giant rollout` must refuse it with a clear error. Used for the meeting's Stage-2-only architecture comparison. | -| `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"flow"` | The generative objective. `"flow"`: conditional flow matching (Lipman et al. 2022), ~10 ODE steps at inference. `"ddpm"`: cosine-schedule diffusion baseline. `"wgan"`: WGAN-GP, single forward pass at inference. Replaces the global `train.mode`. Objective-specific knobs live in the matching sub-table below. | -| `hidden_dim` | int | `256` | Trunk width — also the width of **every expert** under a routed trunk. | -| `n_res_blocks` | int | `6` | Number of `ResBlock`s in the trunk, and in every expert under a routed trunk. Was `model.n_blocks`; renamed for clarity since v0.3.0 also has attention layers in stage 2. | -| `dropout` | float | `0.0` | Dropout inside each `ResBlock`. **Default changed in v0.3.0** (was `0.1`). | -| `lambda` | float | `1.0` | Weight of this stage's loss in the total. Meaningful when both stages are active and non-adversarial; a WGAN stage's adversarial loss drives its own optimizer, so `lambda` scales only its non-adversarial auxiliary terms. | - -#### `[stage1_model.flow]` — read only when `generator = "flow"` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `time_dim` | int | `64` | Width of the `SinusoidalEmbedding` for the flow time variable, concatenated into the trunk's conditioning. **New in v0.3.0** — v0.2 hardcoded 64. | - -#### `[stage1_model.ddpm]` — read only when `generator = "ddpm"` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `time_dim` | int | `64` | As above, for the diffusion time variable. | -| `n_steps` | int | `1000` | Cosine-schedule diffusion steps. **New in v0.3.0** — v0.2 hardcoded this in `CosineSchedule`. | - -#### `[stage1_model.wgan]` — read only when `generator = "wgan"` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `noise_dim` | int | `64` | Width of the generator's input noise vector. There is no time variable, hence no `time_dim`. | -| `n_critic` | int | `5` | Critic updates per generator update (Gulrajani et al. 2017). | -| `gp_weight` | float | `10.0` | Gradient-penalty coefficient. | -| `critic_lr` | float | `0.0` | Critic learning rate. `0.0` means "inherit `train.lr`" — not `None`, since the TOML writer has no null literal to round-trip. | -| `critic_hidden_dim` | int | `0` | Critic trunk width. `0` = inherit `stage1_model.hidden_dim`. **New in v0.3.0** — v0.2 always sized the critic from the generator. | -| `critic_n_res_blocks` | int | `0` | Critic trunk depth. `0` = inherit `stage1_model.n_res_blocks`. | - -#### `[stage1_model.router]` - -Content carries over from v0.2's `[model.router]` unchanged. Reproduced here in -full because the block is now per-stage and its semantics are easy to lose. - -```toml -[stage1_model.router] -enabled = false -type = "energy" -n_experts = 4 -temperature = 0.5 -learn_centers = true -learn_width = false -learn_temperature = false -width_min_ratio = 0.1 -width_max_ratio = 10.0 -lambda_balance = 0.0 -lambda_entropy = 0.0 -lambda_proc = 0.0 -gumbel = false -gumbel_tau_start = 1.0 -gumbel_tau_end = 0.1 -emb_dim = 8 -hidden_dim = 64 -``` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `enabled` | bool | `false` | Replace the monolithic trunk with a mixture of per-expert trunks: soft-mixed over all experts at train time, **top-1 dispatched at eval time** (each row runs exactly one expert). Every expert is `hidden_dim` × `n_res_blocks` — v0.3.0 removes the per-expert sizing keys, so a routed model costs `n_experts` × the monolith's parameters at equal per-row eval cost. Routing buys specialization, not a per-call speedup. | -| `type` | str | `"energy"` | Router impl from `ROUTER_REGISTRY`. `"energy"`: soft turn-on gate over normalized pre-step log-energy. `"pdg"`: gate over a learned PDG embedding. `"process"`: own classifier over pre-step conditioning predicting the step-ending physics process. `"composed"`: joint outer-product gating over multiple axes via `axis{i}_{field}` keys. | -| `n_experts` | int | `4` | Number of experts. For `type = "process"` this doubles as the number of process classes. Ignored for `type = "composed"` (each axis has its own). | -| `temperature` | float | `0.5` | Softmax denominator for the distance-based gate. As `tau -> 0` the gate hardens to nearest-center (Voronoi) selection, which is exactly what eval-time `top1` uses. Energy/pdg routers only. | -| `learn_centers` | bool | `true` | Whether gate centers are `nn.Parameter` or a fixed buffer. | -| `learn_width` | bool | `false` | Give each expert its own learnable width, sigmoid-bounded to `[width_min_ratio, width_max_ratio] * temperature`. Mutually exclusive with `learn_temperature`. | -| `learn_temperature` | bool | `false` | Make the single shared `temperature` learnable, same bounding. Mutually exclusive with `learn_width`. | -| `width_min_ratio` | float | `0.1` | Lower bound multiplier for the above. Must bracket 1.0 with `width_max_ratio` so enabling either mode is a no-op at init. | -| `width_max_ratio` | float | `10.0` | Upper bound multiplier. **Deliberately bounded rather than `softplus`/`exp`:** an unbounded width lets one expert's logit `-d²/width -> 0` almost everywhere, so it wins nearly every row regardless of distance — the same "experts overlap instead of partitioning" failure the router design exists to avoid. | -| `lambda_balance` | float | `0.0` | Importance-CV² load-balancing auxiliary loss weight (Shazeer et al. 2017). **The 2026-07-22 benchmark failure ran with `0.0`; do not repeat that.** | -| `lambda_entropy` | float | `0.0` | Entropy-regularization weight penalizing uniform/collapsed gating. Secondary guard against all experts' widths co-inflating together, which `lambda_balance` cannot see (usage shares stay even throughout that failure). Use with caution: indiscriminate entropy penalties also suppress legitimate ambiguity near a decision boundary. | -| `lambda_proc` | float | `0.0` | Supervised process-classification CE weight. `"process"` router only; `0.0` still trains a working router (the gate gets gradient through the downstream loss) but only `> 0` grounds it in the true `process` label. | -| `gumbel` | bool | `false` | Straight-through Gumbel-softmax train-time combine weights: the forward pass samples a hard one-hot combination (matching eval-time top-1 dispatch exactly) while the backward pass still flows smooth gradient to every expert. Targets the train/eval mismatch. | -| `gumbel_tau_start` | float | `1.0` | Gumbel temperature at step 0, annealed linearly over training. | -| `gumbel_tau_end` | float | `0.1` | Gumbel temperature at the final step. | -| `emb_dim` | int | `8` | The router's **own** pdg (and material) embedding width, separate from the trunk's `ConditionEncoder`. `"pdg"` / `"process"` routers only. | -| `hidden_dim` | int | `64` | The `"process"` router's internal classifier hidden width. | - -**Composed routers** use flat `axis{i}_{field}` keys instead of `type`/`n_experts` -— e.g. `axis0_type = "energy"`, `axis0_n_experts = 4`, `axis1_type = "pdg"`, -`axis1_n_experts = 3`, `axis1_emb_dim = 8`. Indices must be contiguous from 0. -Flat keys keep the block a table of scalars, which the merge machinery relies on. - -**`centers_init`** is not authored by hand: `giant/pipeline.py` populates it for -`type = "energy"` from real data quantiles collected during the existing -normalizer-fitting pass, then writes it into the checkpoint's `model_config`. - -### 3.3 `[stage2_model]` - -Stage 2 predicts `n_sec` and the per-secondary energy/direction/type. - -```toml -[stage2_model] -active = true -decoder = "autoregressive" -generator = "wgan" -hidden_dim = 256 -n_res_blocks = 6 -dropout = 0.0 -lambda = 1.0 -k_max = 15 -context_dim = 64 -stage1_context = "truth" -``` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `active` | bool | `true` | `false` trains stage 1 alone. The checkpoint then has no secondary decoder; `giant rollout` must refuse it, `giant predict` still works. | -| `decoder` | `"one_shot"` \| `"autoregressive"` | `"autoregressive"` | `"one_shot"`: predict all `k_max` slots simultaneously with padded slots masked from the loss — v0.2 behaviour, kept as the baseline arm of the meeting's §7 comparison. `"autoregressive"`: emit one secondary at a time in descending-energy order. | -| `generator` | `"flow"` \| `"ddpm"` \| `"wgan"` | `"wgan"` | As stage 1. Under `"autoregressive"` this is the objective for **each token**: a WGAN token costs one forward pass, a flow token costs ~10 ODE steps. See the cost note in §6.4. | -| `hidden_dim` | int | `256` | Trunk width. | -| `n_res_blocks` | int | `6` | Trunk depth. | -| `dropout` | float | `0.0` | Dropout inside each `ResBlock`. **Default changed in v0.3.0** (was `0.1`). | -| `lambda` | float | `1.0` | Weight of stage 2's loss in the total. Was `train.lambda_s2`. | -| `k_max` | int | `15` | Maximum secondary slots. Under `"one_shot"` this is the fixed output width; under `"autoregressive"` it is a safety cap on the generation loop. Was the global constant `K_MAX` in `giant/constants.py` (max observed `n_sec` is 14 in the PbWO4 dataset, so 15 covers it with one spare). | -| `context_dim` | int | `64` | Width of the projected stage-1 outcome fed into stage 2's conditioning. Was the hardcoded `stage1_proj_dim = 64`. | -| `stage1_context` | `"truth"` \| `"sampled"` | `"truth"` | What stage 2 conditions on during training. `"truth"`: the ground-truth stage-1 target vector, detached — v0.2 behaviour (v0.2 `train.py:254` passes `x1_s1.detach()`), i.e. stage-level teacher forcing. `"sampled"`: stage 1's own sampled output, closing the train/inference gap at the cost of a sampling pass per batch and a moving target early in training. | - -#### `[stage2_model.n_sec]` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `mode` | `"head"` \| `"stop_token"` \| `"truth"` | `"head"` | `"head"`: a classifier over `{0..k_max}` on the condition encoding alone (no diffusion noise), so it is callable independently at inference — v0.2 behaviour, and what the meeting's §3 explicitly decided to keep. `"stop_token"`: an EOS-style implicit stop — **accepted by the schema but not implemented in v0.3.0**, raising a clear "not implemented" error if set (§11.2). The key exists now so landing the mechanism later is not a config break. `"truth"`: take `n_sec` from ground truth — only valid for standalone stage-2 evaluation, never for rollout. | -| `lambda` | float | `0.1` | Cross-entropy weight for the head. Was `train.lambda_nsec`. | - -#### `[stage2_model.particle_type]` - -**The three targets mirror the three conditioning modes of §3.1**, and use the -same names. - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `target` | `"onehot"` \| `"physical"` \| `"embedding"` | `"onehot"` | What the token's type slice *is*. See the table below. | -| `lambda` | float | `1.0` | Loss weight. Under `generator = "flow"`/`"ddpm"` this weights the cross-entropy (`"onehot"`) or regression (`"physical"`/`"embedding"`) term; under `"wgan"` the type is adversarial (§2.1) and this weights only any auxiliary term. | -| `other_policy` | `"sample"` \| `"modal"` \| `"drop"` | `"sample"` | How a predicted "other" class becomes a concrete PDG code at rollout, needed because a secondary's mass/charge feed its own downstream conditioning. `"sample"`: draw from the empirical within-bucket distribution recorded at map-build time. `"modal"`: always the most common member. `"drop"`: discard the secondary. Read only under `target = "onehot"`. **Not decided in the meeting** — see §11. | - -**There is no `n_classes` key.** The class count under `target = "onehot"` is -`conditioning.particle.emb_dim` — the same number that sizes the particle axis -everywhere else. One knob sets the model's particle-type resolution, and the -stage-2 onehot classes are by construction the same classes the conditioning -onehot uses, so an emitted secondary's type is directly consumable as the -conditioning of its own next step with no re-mapping. - -Note the coupling this creates: under `conditioning.particle.type = "physical"`, -`emb_dim` primarily means "sub-MLP output width", yet it still sets the stage-2 -class count. Intentional, but worth knowing when tuning either. - -| target | token type slice | width | training target | inverse map at rollout | -|--------|------------------|-------|-----------------|------------------------| -| `"physical"` | regressed `(log mass, charge)` | `2` | the true PDG's physics values (`giant.particles.particle_mass_charge`) | none needed — mass/charge are used directly; `giant.particles.nearest_known_pdg` gives a reporting-only label. v0.2 behaviour. | -| `"onehot"` | class logits | `conditioning.particle.emb_dim` | true class index | `argmax` -> class -> PDG (via `other_policy` for the "other" bin) | -| `"embedding"` | an `emb_dim`-wide vector | `conditioning.particle.emb_dim` | `emb.weight[class].detach()` | L1-nearest row of `emb.weight` — see below | - -So the type slice is `conditioning.particle.emb_dim` wide for **both** `"onehot"` -and `"embedding"`, and `2` only for `"physical"`. - -#### `target = "embedding"` in detail - -Stage 2 emits a vector that should equal **the conditioning's own particle -embedding** for the secondary's species — the same `nn.Embedding` table -`[conditioning.particle]` builds, not a second one. - -**Requires `conditioning.particle.type = "embedding"`.** There is no table to -match against under `"physical"` or `"onehot"`; reject at config-validation time -with an explicit error. - -**Why detached:** the regression target is `emb.weight[class].detach()`, so the -embedding table receives gradient **only through the conditioning path**, never -through the stage-2 output loss. Without the detach the target moves as the -decoder chases it — the exact failure mode that motivated abandoning the learned -type target in the first place (see `decisions/physical-property-conditioning`). -The detach is what makes this option viable again. - -**Inverse map.** The natural exact-match form - -```python -((out - emb.weight).abs().sum(1) < 1e-6).nonzero() -``` - -is correct as a **round-trip assertion in tests** (encode a known PDG, decode, -recover the same PDG) but **must not be used at inference**: a generative model's -continuous output essentially never lands within `1e-6` of a table row, so it -returns an empty tensor almost always. Inference needs the nearest row: - -```python -pdg_idx = (out.unsqueeze(-2) - emb.weight).abs().sum(-1).argmin(-1) # L1 nearest -``` - -Note this decode is **unbounded in vocabulary**, unlike `"onehot"` — every PDG -code in the training vocab is reachable, and there is no "other" bucket, hence no -`other_policy`. The trade-off is that nearest-neighbour decode has no notion of -confidence: an output far from every row still snaps to something. - -#### `[stage2_model.autoregressive]` — read only when `decoder = "autoregressive"` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `order` | `"energy_desc"` | `"energy_desc"` | Canonical generation order. Descending energy is the ordering already flagged as natural in the Phase-2 note's open questions, and the one the existing stick-breaking encoding assumes. Single-valued for now; the key exists so an alternative ordering is not a config break. | -| `history` | `"markov"` \| `"attention"` | `"markov"` | How token *i+1* sees tokens ≤ *i*. `"markov"`: previous token plus running scalars (remaining energy budget, slot index) — a fixed-width summary. `"attention"`: causal self-attention over all emitted tokens. See §6.2 for the trade-off. | -| `teacher_forcing` | `"always"` \| `"scheduled"` \| `"never"` | `"always"` | `"always"`: condition on the ground-truth previous secondary throughout training (the meeting's confirmed plan). `"scheduled"`: scheduled sampling — interpolate toward conditioning on the model's own prediction. `"never"`: free-running from the start. | -| `tf_p_start` | float | `1.0` | Under `"scheduled"`, P(use ground truth) at epoch 0. | -| `tf_p_end` | float | `1.0` | Under `"scheduled"`, P(use ground truth) at the final epoch. Linear interpolation between the two. | -| `attn_n_heads` | int | `4` | Attention heads. Read only under `history = "attention"`. | -| `attn_n_layers` | int | `2` | Causal self-attention layers. Read only under `history = "attention"`. | - -#### `[stage2_model.flow]` / `[stage2_model.ddpm]` - -Same keys as their `[stage1_model.*]` counterparts (`time_dim`; plus `n_steps` -for ddpm). - -#### `[stage2_model.wgan]` — read only when `generator = "wgan"` - -Same keys as `[stage1_model.wgan]` (`noise_dim`, `n_critic`, `gp_weight`, -`critic_lr`, `critic_hidden_dim`, `critic_n_res_blocks`), plus: - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `gumbel_tau_start` | float | `1.0` | Straight-through Gumbel temperature for the **particle-type one-hot** at step 0, annealed linearly. Read only under `particle_type.target = "onehot"` (the other two targets are continuous and need no relaxation). Distinct from `router.gumbel_tau_start`, which anneals expert-combination weights — two unrelated Gumbel relaxations that must not share a key. | -| `gumbel_tau_end` | float | `0.1` | Same, at the final step. | - -Under `"autoregressive"` a fresh `noise_dim` draw is made **per token**. - -#### `[stage2_model.router]` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `tie_to_stage1` | bool | `false` | `true`: stage 2 shares **stage 1's `Router` module instance**, so expert *i* in stage 1 and expert *i* in stage 2 gate on identical conditions by construction. Every other key in this block is then ignored. `false`: an independent router — note this is v0.2's actual behaviour, which built *two separate routers from one config*, so stage-1 expert *i* and stage-2 expert *i* had no semantic relationship despite identical hyperparameters. | - -All other keys are as `[stage1_model.router]`. Invalid when `stage1_model.active -= false` and `tie_to_stage1 = true` — reject at config-validation time. - -### 3.4 `[train]` - -Optimizer, schedule, data split and logging only. Everything model-shaped moved -into the stage blocks. - -```toml -[train] -epochs = 100 -batch_size = 4096 -lr = 3e-4 -weight_decay = 0.01 -ema_decay = 0.9999 -warmup_epochs = 5 -val_fraction = 0.1 -max_val_batches = 200 -num_workers = 4 -seed = 0 -validate_every = 10 -validate_steps = 10 -wandb = true -wandb_project = "giant" -wandb_run_name = "" -wandb_log_every = 50 -``` - -| Key | Type | Default | Meaning | -|-----|------|---------|---------| -| `epochs` | int | `100` | Training epochs. | -| `batch_size` | int | `4096` | Steps per batch. `auto` on the CLI estimates from free VRAM. | -| `lr` | float | `3e-4` | AdamW learning rate for every stage's generator. | -| `weight_decay` | float | `0.01` | AdamW weight decay. | -| `ema_decay` | float | `0.9999` | EMA of model weights used for sampling; `0` disables. Maintained per stage. | -| `warmup_epochs` | int | `5` | Linear LR warmup. | -| `val_fraction` | float | `0.1` | Fraction of **events** (not steps) held out — the split is by `event_id` to avoid leaking correlated steps from the same shower. | -| `max_val_batches` | int | `200` | Cap on the per-epoch val-loss pass; `0` = full val set. Distinct from `validate_every`'s marginal/KL pass. | -| `num_workers` | int | `4` | DataLoader workers. Warns above ~1/4 of the machine's CPUs — portal machines are shared. | -| `seed` | int | `0` | Seeds Python/numpy/torch and the event split. | -| `validate_every` | int | `10` | Epochs between full marginal/KL validation passes. | -| `validate_steps` | int | `10` | Sampler steps used during those passes. | -| `wandb` | bool | `true` | W&B per-epoch metric logging. **Default changed in v0.3.0** (was opt-in `false`): the v0.3.0 work is a sequence of architecture comparisons, and a run that was not logged is not comparable. Set `false` for throwaway/debug runs. | -| `wandb_project` | str | `"giant"` | W&B project. | -| `wandb_run_name` | str | `""` | `""` means "use the checkpoint out_dir name" — not `None`, since the TOML writer has no null literal. | -| `wandb_log_every` | int | `50` | Optimizer steps between batch-granularity metric logs. A single epoch can be tens of thousands of steps; per-epoch metrics always log in full. | - -### 3.5 Removed from v0.2 - -| Key | Fate | -|-----|------| -| `train.mode` | Split into `stage1_model.generator` / `stage2_model.generator`. | -| `train.lambda_nsec` | -> `stage2_model.n_sec.lambda`. | -| `train.lambda_s2` | -> `stage2_model.lambda`. | -| `train.n_critic`, `gp_weight`, `critic_lr` | -> `stage{1,2}_model.wgan.*`. | -| `[model]` (whole block) | Split across `[conditioning]` and the two stage blocks. | - ---- - -## 4. Migration: v0.2 -> v0.3 - -`[meta] config_version = 3` tags the new format; **absent means v0.2**. -`migrate_config(cfg) -> cfg` applies the table below and is called on both -`config.toml` load and checkpoint `model_config` load, so nothing on `/ceph` goes -dead (decision 3). - -| v0.2 | v0.3 | -|------|------| -| `train.mode` | `stage1_model.generator` **and** `stage2_model.generator` (same value) | -| `train.lambda_nsec` | `stage2_model.n_sec.lambda` | -| `train.lambda_s2` | `stage2_model.lambda` | -| `train.n_critic` / `gp_weight` / `critic_lr` | `stage1_model.wgan.*` **and** `stage2_model.wgan.*` | -| `model.hidden_dim` / `n_blocks` / `dropout` | `stage{1,2}_model.hidden_dim` / `n_res_blocks` / `dropout` | -| `model.emb_dim` | `conditioning.particle.emb_dim` **and** `conditioning.material.emb_dim` (v0.2 had one shared value) | -| `model.conditioning` | `conditioning.particle.type` **and** `conditioning.material.type` (v0.2 had one shared mode) | -| `model.noise_dim` | `stage{1,2}_model.wgan.noise_dim` | -| `model.router.*` | `stage1_model.router.*`, copied verbatim to `stage2_model.router.*` with `tie_to_stage1 = false` (preserves v0.2's two-independent-routers behaviour) | -| `model.expert_hidden_dim` / `expert_n_blocks` | **dropped.** v0.2's `0` sentinel meant "inherit from the monolith", which is now unconditional. A v0.2 config with a *non-zero* value must fail loudly rather than silently resize the experts — see §4.3. | -| `model.k_max` | `stage2_model.k_max` | -| — | `conditioning.out_dim = 128` (v0.2's hardcoded value) | -| — | `conditioning.{particle,material}.n_layers = 2` (v0.2's hardcoded depth; note the v0.3 *default* is 1) | -| — | `stage{1,2}_model.flow.time_dim = 64` / `.ddpm.time_dim = 64` (v0.2's hardcoded value) | -| — | `stage2_model.context_dim = 64` (v0.2's hardcoded `stage1_proj_dim`) | -| — | `stage2_model.decoder = "one_shot"` (v0.2 had no other option) | -| — | `stage2_model.particle_type.target = "physical"` (v0.2 behaviour) | -| — | `stage{1,2}_model.active = true` | - -### 4.1 The awkward one: `n_sec` head ownership - -Decision 1 moves the `n_sec` head to stage 2, but **v0.2 checkpoints carry -`n_sec_head` weights inside the stage-1 module** (`DenoisingMLP.n_sec_head`, -`WGANGenerator.n_sec_head`, `RoutedDenoisingMLP.n_sec_head`). The shim must keep -those loading where they are. - -Handling: `migrate_config` sets an internal -`stage2_model.n_sec.legacy_owner = "stage1"` that `build_models` honours by -attaching the head to the stage-1 module. Never written by new runs, never -CLI-settable, never documented as a user-facing option. - -### 4.2 Non-inheriting expert dims - -v0.3.0 drops `expert_hidden_dim` / `expert_n_blocks` (§2.2). Migration must -distinguish two cases: - -- value is `0` (the "inherit" sentinel, and what every real run used) — drop the - key silently, behaviour is unchanged. -- value is non-zero — **fail loudly.** Silently resizing those experts to - `hidden_dim` would change the architecture, so the checkpoint's weights would no - longer match. Such a checkpoint can only be loaded by v0.2. - -### 4.3 Migration test - -The acceptance criterion for the whole shim: **load a v0.2 checkpoint through -`migrate_config` + the new `build_models`, and diff its outputs against v0.2 code -on the same input batch.** Bit-identical, or the refactor has changed something it -should not have. Pick one flow checkpoint and one WGAN checkpoint from `/ceph`. - ---- - -## 5. network.py refactor - -### 5.1 What it looks like today - -Ten classes that are permutations of three independent choices: - -| | flow/ddpm | wgan generator | wgan critic | -|---|---|---|---| -| **stage 1** | `DenoisingMLP` | `WGANGenerator` | `Critic` | -| **stage 1, routed** | `RoutedDenoisingMLP` | — | — | -| **stage 2** | `SecondaryDecoder` | `WGANSecondaryGenerator` | `SecondaryCritic` | -| **stage 2, routed** | `RoutedSecondaryDecoder` | — | — | - -The empty cells are the entire reason `giant/pipeline.py:275` hard-rejects -`--mode wgan --router`: no routed WGAN generator class was ever written. There is -no deeper reason — the routed trunk is orthogonal to the objective. - -Every one of those classes repeats the same body: build a condition encoder, -optionally a time embedding, project input, run blocks, project output. - -### 5.2 Proposed decomposition — one axis per config block - -**(a) `[conditioning]` -> encoders** - -```python -ConditionEncoder(type, emb_dim, n_layers, out_dim) # behaviour unchanged, now configurable -ContextAdapter(in_dim, context_dim) # stage-1 outcome -> context vector -``` - -`SecondaryConditionEncoder` **disappears as a class**. It was -`ConditionEncoder` + a `stage1_proj` linear + a fuse layer; those compose at the -stage-model level instead. - -**(b) `[stage*_model]` + `.router` -> trunks, behind one interface** - -```python -class Trunk(nn.Module): - def forward(self, x, cond, cond_cont=None, cond_cat=None) -> Tensor: ... - -MonolithicTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) -RoutedTrunk(router, in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) - -build_trunk(stage_cfg, in_dim, out_dim, cond_dim) -> Trunk -``` - -`ExpertTrunk` and `_route_forward` carry over unchanged. **One required change:** -`MonolithicTrunk` and `ExpertTrunk` need a separate `out_dim` — today -`ExpertTrunk` hardcodes `out_proj = nn.Linear(hidden_dim, in_dim)`, i.e. -`out_dim == in_dim`. That stops working the moment stage 2's per-token output is -`4 + type_dim` wide while its input is a noise vector of width `noise_dim`. - -**(c) `[stage*_model].generator` -> a thin wrapper, not a class family** - -The generator choice controls exactly two things: - -- whether `SinusoidalEmbedding(t)` is concatenated into `cond` (flow/ddpm) or not (wgan) -- whether the trunk's `x` is the diffused/interpolated `x_t` (flow/ddpm) or a noise draw `z` (wgan) - -That is small enough for one wrapper, collapsing six of today's ten classes: - -```python -class GenerativeTrunk(nn.Module): - """cond-encode -> (optional time-embed) -> trunk. Backs flow, ddpm and wgan.""" - def forward(self, x, cond_cont, cond_cat, t=None, context=None) -> Tensor: ... -``` - -### 5.3 Resulting class list - -``` -# building blocks -SinusoidalEmbedding, ResBlock, ConditionEncoder, ContextAdapter - -# trunks -Trunk (ABC), MonolithicTrunk, RoutedTrunk, ExpertTrunk - -# routers — carried over unchanged -Router, EnergyRouter, PdgRouter, ProcessRouter, ComposedRouter -ROUTER_REGISTRY, register_router, build_router, build_composed_router - -# history encoders — new, stage-2 AR only -HistoryEncoder (ABC), MarkovHistory, AttentionHistory - -# stage models -Stage1Model # 9D primary step -Stage2OneShot # k_max slots at once (v0.2 behaviour) -Stage2Autoregressive # one token at a time -CriticModel # stage-1 or stage-2 critic, generator-agnostic - -# factories -build_models(cfg) -> {"stage1": ... | None, "stage2": ... | None} -build_critics(cfg) -> {"stage1": ... | None, "stage2": ... | None} -``` - -Ten classes become four stage classes plus reusable parts, and **routed WGAN comes -for free** — `pipeline.py`'s rejection can be deleted. - -### 5.4 Factory signature change - -`build_models` returns a **dict, not a tuple**: `active = false` on either stage -means that key is `None`. Every caller unpacking -`stage1, sec_decoder = build_models(...)` must be updated -(`pipeline.py:390`, `cli.py:871`, `cli.py:1254`). - ---- - -## 6. Stage-2 autoregressive design - -### 6.1 Token layout - -Secondaries are emitted one at a time in descending energy order. Per-token output -width is `4 + type_dim`: - -| slice | meaning | -|-------|---------| -| `[0]` | stick-breaking logit — fraction of the **remaining** energy budget | -| `[1:4]` | local-frame direction, normalized to a unit vector | -| `[4:4+type_dim]` | particle type — see below | - -`type_dim` follows `particle_type.target` (§3.3): `2` for `"physical"`, and -`conditioning.particle.emb_dim` for both `"onehot"` and `"embedding"`. -Only `"onehot"` involves a relaxation — ST-Gumbel under `wgan`, cross-entropy -under `flow`/`ddpm`; the other two are continuous and feed the critic (or the -regression loss) directly. - -Per-token conditioning is: - -``` -base condition encoding (ConditionEncoder, [conditioning].out_dim) -+ stage-1 context (ContextAdapter, stage2_model.context_dim) -+ history_encoder(prefix) (§6.2) -+ running scalars (remaining energy budget, slot index) -``` - -### 6.2 History encoders - -One interface, `history_encoder(prefix) -> fixed-width vector`: - -- **`MarkovHistory`** — the previous token's `(energy_fraction, direction, - type_embedding)`. Fixed-width, one small MLP. -- **`AttentionHistory`** — causal self-attention over all emitted tokens, taking - the last position. `attn_n_heads` × `attn_n_layers`. - -**The trade-off** (this is decision 8's reasoning, recorded so it does not have to -be re-derived): - -1. **Markov is less impoverished than it sounds.** The remaining-energy budget is - an *exact sufficient statistic* for the conservation constraint — stick-breaking - needs nothing else from history. Slot index likewise. What markov genuinely - cannot see is set *composition*: "I have already emitted two photons and an - electron." That matters for correlated production — pair production emits - exactly e⁺e⁻, a brems cascade correlates species across the set. Note that the - meeting's charge-conservation idea was exactly such a hand-engineered summary - statistic, and it is out of scope for v0.3.0 (decision 6), so markov does not - get that crutch. -2. **Sequences are short and front-loaded.** `n_sec` is 0–2 for most steps, max 14. - For K ≤ 2, attention *is* markov. They diverge only in the high-multiplicity - tail — physically interesting but data-poor, so the attention path trains on - few examples where it actually matters. -3. **Cost is not where intuition puts it.** Under teacher forcing both train in a - single parallel pass over all K tokens (every token's input is ground truth, so - nothing is sequential). At inference both need K sequential forwards; attention - additionally needs a KV cache to avoid re-encoding the prefix. Attention's - marginal FLOPs over ≤15 tokens are rounding error. -4. **Exposure bias cuts against attention.** Attention conditions on the entire - generated prefix, so one off-manifold early token poisons every later token - through the context. Markov's fixed summary sees only one bad token, and the - budget scalars stay exact regardless. Given the explicitly-flagged train/ - inference gap and the known compounding-rollout-error problem, the more - expressive history is also the more fragile one. - -Hence: markov is the default and the baseline; attention is a flag. - -### 6.3 Energy budget under AR - -**No re-derivation needed**, despite the meeting's action item suggesting -otherwise. The existing stick-breaking is already sequential in spirit — each slot -takes a fraction of what remains — so it carries over to per-token generation -directly by feeding "remaining budget" as a per-token conditioning scalar. -Conservation stays exact by construction: the valid slots' energies sum to `e_sec`, -which the Stage-1 simplex already guarantees sums correctly with `edep` and -`post_E`. - -### 6.4 Cost warning - -AR costs **K sequential forward passes per step** where one-shot costs one. -Against the ~10× native-Geant4 eval budget that motivated the whole fast-eval -track, this is the number to watch — not the history-encoder choice. With -`generator = "flow"` it is worse still: ~10 ODE steps per token, so ~150 forwards -per step in the worst case. `generator = "wgan"` (one pass per token) is the only -configuration that plausibly meets the budget; flow AR is for quality comparison. - ---- - -## 7. Training loop - -Decision 2 (full mixed per-stage objectives) makes `giant/training/` one trainer object -per active stage: - -```python -class StageTrainer: # owns optimizers, EMA, update cadence - def step(self, batch, global_step) -> dict[str, float]: ... - -FlowTrainer, DDPMTrainer, WGANTrainer(critic, n_critic, gp_weight) -``` - -- Non-adversarial stages contribute `lambda * loss` to one backward pass. -- A WGAN stage runs its own critic inner loop on the same batch, with a generator - update every `n_critic`-th batch — today's `_wgan_train_step` cadence. -- A mixed run (`flow` + `wgan`) steps stage 1 every batch while stage 2 does 5 - critic updates then a generator update. Independent optimizers, independent EMA. -- Router auxiliary losses (`lambda_balance` / `lambda_proc` / `lambda_entropy`) - become per-stage, summed over whichever stages are routed. Today's - `hasattr(model, "router")` check (v0.2 `train.py:262`) generalizes cleanly. -- `metrics.csv` and W&B metric names gain a stage prefix. **Implemented as** - `//` (`stage1/train/loss`, `stage2/train/d_loss`, - `stage1/lr`, `stage1/router/entropy`) plus an unprefixed run-level tail - (`val/loss`, `val/marginal_kl`, `grad_norm`, `gpu_mem_mb`, - `samples_per_sec`, `is_best`, `epoch_time_s`). Each name is declared once, - as a `MetricSpec` on the `StageTrainer` that computes it; the CSV header - and W&B payload are derived from those declarations - (`giant/training/metrics.py`). -- With `stage1_model.active = false`, stage 2 still needs its stage-1 context: it - comes from the ground-truth target already in the batch (`x1_s1`), which is - exactly what v0.2 does anyway. **Stage-2-only training is therefore a cheap - ablation, not new plumbing** — drop the stage-1 loss, skip building stage 1. - ---- - -## 8. Data and setup-cache changes - -Three config options need a "top N−1 by training-set count plus other" map, but -they resolve to **at most two distinct maps per run** — one per axis — because -the class count always comes from that axis's `emb_dim`: - -| consumer | axis | N | -|----------|------|---| -| `conditioning.particle.type = "onehot"` | PDG | `conditioning.particle.emb_dim` | -| `stage2_model.particle_type.target = "onehot"` | PDG | `conditioning.particle.emb_dim` | -| `conditioning.material.type = "onehot"` | material | `conditioning.material.emb_dim` | - -The two PDG consumers therefore **share one map** — which is the point of -dropping `n_classes`: a secondary's emitted type is directly consumable as the -conditioning of its own next step, with no re-mapping between two class systems. -Build one shared helper, structurally identical to today's `proc_map`: - -- `build_topn_map_from_files(files, column, n_classes=...)` in - `giant/data/loader.py`, next to `build_process_map_from_files` -- a `setup_cache` section keyed by `(axis, N)`, same shape as `cache.proc_maps` - (which is keyed by `n_experts`). N is still part of the key so the sidecar stays - reusable across runs with different `emb_dim`, even though a single run only - ever needs one N per axis. -- persisted into the checkpoint beside `pdg_map` / `mat_map` -- inverted at rollout to recover a concrete PDG -> mass/charge per secondary - -Record the **empirical within-bucket distribution** at map-build time as well — -`other_policy = "sample"` needs it. - -`particle_type.target = "embedding"` needs no map: it reaches the full training -vocab through the conditioning's embedding table (§3.3). - -`giant/constants.py`: `K_MAX` and `SEC_DIM` stop being authoritative constants -(they become `stage2_model.k_max` and a derived quantity). Keep them as defaults -only, and audit the ~12 modules importing `K_MAX` for places that assume it is -global truth. - ---- - -## 9. Config machinery changes - -All in `giant/config.py`: - -- **`merge_cli_overrides`** — replace the hand-written one-level router merge with - a generic recursive deep-merge. The new layout is three levels deep - (`stage1_model.router.axis0_type`). -- **`save_config`** — recursive TOML writer; today it handles exactly one nesting - level (see its `nested_sections` list). -- **`default_out_dir_name`** — `_OUT_DIR_NAME_CANDIDATES` entries become dotted - paths (`"stage2_model.decoder"`) instead of `(section, field)` pairs. Add - candidates for the new discriminating fields: `decoder`, per-stage `generator`, - `particle_type.target`, `autoregressive.history`. -- **`resolve_expert_dims`** — **deleted.** Experts always take the stage's - `hidden_dim` / `n_res_blocks` (§2.2). Its two callers (`pipeline.py:351` and the - CLI's batch-size auto-estimate) read the stage keys directly, as does - `pipeline.py`'s "experts are NxM, different from model.hidden_dim" warning, - which becomes unreachable and should go. -- **`migrate_config(cfg) -> cfg`** — §4's table, applied on both `config.toml` load - and checkpoint `model_config` load. New `[meta] config_version = 3`. -- **`Conditioning` enum** — gains a third member `onehot`, and now feeds **two** - keys (`conditioning.particle.type`, `conditioning.material.type`) rather than - one. Shared with `scripts/dwarf.py`, so both CLIs stay in sync. -- **Cross-block validation** — a new `validate_config(cfg)` pass, since v0.3.0 has - constraints no single block can check: - `particle_type.target = "embedding"` requires - `conditioning.particle.type = "embedding"`; router types `"pdg"`/`"process"` - require `conditioning.particle.type != "physical"`; - `stage2_model.router.tie_to_stage1` requires `stage1_model.active`; - `n_sec.mode = "truth"` is invalid for a rollout-capable checkpoint. -- **`estimate_batch_size`** — its calibration constants assume the v0.2 - architecture ("post-Phase-2, including the Stage-2 secondary decoder and n_sec - head"). AR stage 2 changes the activation-memory profile. **Recalibrate last** - (§12 step 8), measured on real hardware with the example configs. - ---- - -## 10. Callers that need updating - -| File | Why | -|------|-----| -| `giant/pipeline.py` | Builds `model_config`; now per-stage. Delete the wgan+router rejection at `:275`. Router `centers_init` seeding becomes per-stage. | -| `giant/training/` | Per-stage trainers (§7), metric collection, checkpointing, the epoch loop. | -| `giant/cli.py` | Stage-prefixed flags for `train` and `new-run`; `build_models` now returns a dict (`:871`, `:1254`); `:1339` writes `model_config` into the rollout sidecar. | -| `giant/sample.py` | Sampler picked per stage from `stage*_model.generator`; new AR sampling loop with KV cache under `history = "attention"`. | -| `giant/rollout.py` | AR secondary generation; categorical class -> PDG decode; `other_policy` handling. | -| `giant/validate.py` | Stage-2 marginals gain a type-class marginal. | -| `giant/analysis/render.py` | `_router_summary(model_config)` at `:44` reads `model_config["router"]`. | -| `giant/analysis/router_gating.py` | Same, at `:76`. | -| `giant/constants.py` | `K_MAX` / `SEC_DIM` demoted to defaults (§8). | -| `configs/*.toml` | All eight shipped configs are v0.2-format; regenerate or rely on the shim. | -| `condor-gpu-train-rollout` branch | Submits `giant train` flags; needs rebasing onto the new flag surface. | - ---- - -## 11. Settled scope and open questions - -### 11.1 Settled — build as specified - -- **`other_policy` switch.** The three-way `"sample"` / `"modal"` / `"drop"` - design in §3.3 is approved as the config surface for turning a predicted "other" - class into a concrete PDG at rollout. `"sample"` (draw from the empirical - within-bucket distribution recorded at map-build time) stays the default as the - least-biased option. -- **`[stage*_model.flow]` and `[stage*_model.ddpm]` stay separate tables.** The - duplicated `time_dim` is accepted; a merged `[stage*_model.diffusion]` was - considered and rejected because the name fits flow matching poorly. - -### 11.2 Deferred — not implemented in v0.3.0 - -- **`n_sec.mode = "stop_token"`.** The value is **accepted by the schema** but - raises a clear "not implemented in v0.3.0" error if set. The key existing now - means landing the implicit-stop mechanism later is not a config break. `"head"` - is what v0.3.0 builds, per the meeting's §3 decision. -- **Charge conservation.** No key at all — `[stage2_model.conservation]` is absent - from v0.3.0 (decision 6), unlike `stop_token` above. **Deliberately left - undesigned:** the mechanism should be worked out on its own terms when it is - taken up, not pre-shaped by choices made for this refactor. Adding the block - later is a config addition, not a break. -- **`stage2_model.generator = "ddpm"`.** The value is **accepted by the - schema** (§3.3 lists `"flow" | "ddpm" | "wgan"` with no caveat) but - `FlowDDPMStageTrainer.__init__` (`giant/training/trainers.py`) raises - `NotImplementedError` for stage 2 — only `"flow"` and `"wgan"` have a - stage-2 secondary-decoder loss implemented. `stage1_model.generator = - "ddpm"` is unaffected; this restriction is stage-2-only. Landing stage-2 - ddpm later is a trainer addition, not a config break. - -### 11.3 Tracked as implementation work - -- **Log the L1 distance distribution at rollout** under - `particle_type.target = "embedding"`. Nearest-neighbour decode has no reject - option — an output far from every table row still snaps to its nearest neighbour, - with no "other" bin and no confidence signal. A heavy tail in that distribution - means the decoder is emitting vectors off the embedding manifold, which is the - direct analogue of the species-collapse symptom this redesign exists to fix. - Belongs with the `giant/rollout.py` decode work (step 6) and should surface as a - `giant analyze` diagnostic plot alongside `router_gating`. -- **`estimate_batch_size` recalibration** for AR stage 2 is the **last** step of - the implementation flow (§12 step 8), measured on real hardware using the example - configs — not guessed from the existing calibration constants. - -### 11.4 Accepted with a validation obligation: differentiability - -**Position taken: differentiability through the categorical type path is broken, -and that is accepted.** The expected contribution of the broken path to the total -gradient is small enough to ignore. **This is an assumption, not a result — it has -to be demonstrated later.** - -Recording it precisely, since "broken" covers three distinct things: - -| where | status | -|-------|--------| -| Per-token training loss under teacher forcing | **Fine.** Softmax cross-entropy needs no sampling; the loss is differentiable in the logits. | -| ST-Gumbel into the critic (`generator = "wgan"`, §2.1) | **Biased, not absent.** The forward pass is a hard one-hot; the backward pass pretends it was the soft sample. Gradient flows, but it is not the gradient of what was actually computed. | -| Full shower-rollout backprop | **Structurally broken regardless.** Already non-differentiable once secondaries spawn branches, independent of the type representation — so the categorical switch costs nothing that was not already lost. | - -The claim being accepted is about the middle row: the straight-through estimator's -bias, propagated into the shared trunk, is expected to be negligible against the -gradient from the continuous paths (stick-breaking energy, direction, and — when -stage 1 is active — the 9D primary target). The third row is the reason this is -tolerable at all: end-to-end differentiability was never available. - -**Validation obligation.** Do not treat this as settled until one of the following -has actually been run. Cheapest first: - -1. **Gradient-magnitude accounting.** Instrument a training run to log the norm of - the trunk gradient contributed through the type slice against the norm from the - continuous slices. "Negligible" should mean a stable, small ratio — not merely - small at initialization. This is the direct measurement of the claim and costs - almost nothing to add. -2. **Detached-type ablation.** Train with the type path detached from the shared - trunk entirely (type head still learns; no type gradient reaches the trunk) - against the ST-Gumbel default. Comparable species marginals and kinematics mean - the coupling was weak, which is the same conclusion by a different route. -3. **Estimator swap**, only if 1–2 are inconclusive: compare ST-Gumbel against an - unbiased-but-high-variance estimator (e.g. REINFORCE with a baseline) on a short - run. Agreement in the learned marginals means the bias did not matter. - -Option 1 should be added when the AR trunk lands (step 5), so the evidence accrues -during the architecture comparison rather than needing a dedicated run afterwards. - -**Why it still matters that this is written down:** decision 5 (adversarial type -via ST-Gumbel) rests on this assumption. If the ratio in test 1 turns out not to be -small, the fallback is not a redesign — it is `particle_type.target = "physical"` -or a non-adversarial CE head, both of which already exist as config options. - ---- - -## 12. Implementation order - -1. **`config.py`** — new `DEFAULT_CONFIG`, recursive merge/write, `migrate_config`, - tests. Nothing else can land first. -2. **`network.py`** — the §5 decomposition, with `Stage2OneShot` reproducing v0.2 - exactly. Gate on the §4.3 migration test: load a v0.2 checkpoint through the - shim and diff outputs against v0.2 code. -3. **`giant/training/`** — per-stage trainers; `active = false` paths. At this point - Stage-2-only training works and the meeting's step 2 (one-shot WGAN baseline, - trained standalone) is runnable. -4. **Type map** — `loader.py` + `setup_cache.py` + checkpoint persistence + - `particle_type.target = "onehot"` in `Stage2OneShot`. This is the meeting's - action item 1, and it is testable against the one-shot baseline before any AR - work. -5. **`Stage2Autoregressive`** with `history = "markov"`, `teacher_forcing = - "always"`. The meeting's step 3. Add the gradient-magnitude instrumentation of - §11.4 test 1 here, so the differentiability assumption accrues evidence during - the architecture comparison instead of needing its own run later. -6. **`sample.py` / `rollout.py`** — AR generation and class -> PDG decode, so an AR - model can actually be rolled out and put through `giant analyze`. Includes the - L1-distance diagnostic of §11.3. -7. **`history = "attention"`, scheduled sampling** — then run the meeting's §7 - comparison (one-shot vs autoregressive, standalone) and only chain the winner - behind Stage 1. -8. **`estimate_batch_size` recalibration** — last, once the architectures are - settled. Measure on real hardware with the example configs and record the new - calibration points in `config.py` (§9, §11.3). - -Steps 1–3 are pure refactor with a bit-identical acceptance criterion. Steps 4–7 -are the actual physics change. Step 8 is measurement, deliberately last: the -activation-memory profile is not knowable until the AR trunk and its history -encoder are final. diff --git a/docs/v0.3.0-followups.md b/docs/v0.3.0-followups.md deleted file mode 100644 index 9a9caef..0000000 --- a/docs/v0.3.0-followups.md +++ /dev/null @@ -1,53 +0,0 @@ -# v0.3.0 — post-implementation audit: open discrepancies - -**Status:** steps 1–7 of `docs/v0.3.0-design.md` §12 are implemented (branch -`v0.3.0-stage2-autoregressive`, commits `eb6dd27`..`200c6d2`). This document -tracked discrepancies found between that implementation and the design contract -during a 2026-08-07 audit, as concrete work items. **All items (1-9) are now -resolved** — either implemented (1-5, 7-9) or explicitly deferred into -`docs/v0.3.0-design.md` §11.2 (6: `stage2_model.generator = "ddpm"`). Step 8 -(`estimate_batch_size` recalibration) was intentionally still outstanding per -§12 and was never tracked here. - ---- - -## Confirmed correct during the audit (no action needed) - -For reference — these were explicitly checked against the design doc and -match it, including two spots the doc itself flagged as likely stale that -turned out fine: - -- Config schema, `DEFAULT_CONFIG`, `migrate_config` table (§4), `save_config`/ - `merge_cli_overrides` recursion, `default_out_dir_name`, `Conditioning` enum, - `n_sec.mode = "stop_token"` error (§9, §11.2). -- `network.py`'s full class decomposition (§5.3), dict-returning - `build_models`/`build_critics` (§5.4), `ExpertTrunk` separate in/out dims, - ST-Gumbel wiring (§2.1), AR token layout (§6.1), Markov/Attention history - encoders (§6.2). -- Shared PDG top-N type map (one map, not two, per §8), `other_policy` - sample/modal/drop (§11.1), embedding L1-nearest decode, and the L1-distance - diagnostic surfaced in `giant analyze` (§11.3). -- `analysis/render.py`/`analysis/router_gating.py` correctly branch - old-flat vs new-nested `model_config["router"]` location — doc flagged this - as a likely stale spot (§10) but it's actually fine. -- `giant/training/`'s per-stage trainers, mixed flow+wgan runs, WGAN critic cadence, - per-stage router auxiliary losses, stage-prefixed metrics, stage-2-only - training via ground-truth `x1_s1` (§7). - -## Behavior change: WGAN best-checkpoint selection - -The `giant/training/` split fixed a dead guard in WGAN validation scoring. A -WGAN stage was *meant* to contribute its marginal KL to the `val_loss` that -drives `best.pt`, but the guard `if n not in val_loss_per_stage` could never -fire (every stage was pre-seeded to `0.0`), so the stage contributed a flat -`0.0` and the KL was written to `metrics.csv` without ever being used. -`WGANStageTrainer.val_objective` now returns the KL as intended. - -**Consequence:** any checkpoint selected before this commit under a config -with a WGAN stage — including the v0.3.0 default (`stage2_model.generator = -"wgan"`) — picked its best epoch on the non-adversarial stages alone. Measured -on the test harness's default flow+wgan config, `val_loss` went from `2.182` -(stage 1 only) to `15.137` (stage 1 + KL `12.954`), and which epoch won -changed. Do not compare `val/loss` or `best.pt` choice across this commit. -- `pipeline.py`'s deleted wgan+router rejection, per-stage `centers_init` - seeding, removed stale expert-size warning (§9, §10). diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index 8f5c751..a4a48d2 100644 --- a/giant/analysis/catalog.py +++ b/giant/analysis/catalog.py @@ -72,7 +72,7 @@ class Bundle: r_phys: pl.LazyFrame # rollout, physical steps only t_phys: pl.LazyFrame # reference, physical steps only checkpoint: str | None = None # from the rollout YAML; router_gating only - # §11.3 diagnostic pre-aggregated at rollout time (giant.rollout. + # Diagnostic pre-aggregated at rollout time (giant.rollout. # L1DistCollector.summary()) — from the rollout YAML, type_embedding_l1_distance # only. Unlike checkpoint/router_gating, this needs no live model: it's # already a finished histogram, just passed through. diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index ec2d3e0..9cd1c64 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -83,7 +83,7 @@ _PLOT_META_KEYS = ( "best_val_loss", "training_config", "training_meta", - # §11.3 diagnostic — only present when giant rollout ran under + # Diagnostic — only present when giant rollout ran under # stage2_model.particle_type.target="embedding" (see giant/cli.py's # rollout command and giant.rollout.L1DistCollector); absent otherwise, # which the type_embedding_l1_distance PlotSpec (catalog.py) reads as diff --git a/giant/analysis/render.py b/giant/analysis/render.py index ec166ca..862e023 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -51,8 +51,8 @@ def _figure_params_v2(mc: dict, run_meta: dict) -> dict: """`_figure_params` for a new-shape (nested) `model_config` — has a `stage1_model` key. Reports stage 1's architecture (the headline generator); stage 2's generator is only added (`mode_s2`) when it - differs from stage 1's, since a mixed run (docs/v0.3.0-design.md's - `stage1=flow` + `stage2=wgan` case) is the interesting exception, not + differs from stage 1's, since a mixed run (the `stage1=flow` + + `stage2=wgan` case) is the interesting exception, not the common case.""" s1 = mc["stage1_model"] s2 = mc.get("stage2_model") or {} diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index edfb826..491a433 100644 --- a/giant/analysis/router_gating.py +++ b/giant/analysis/router_gating.py @@ -71,8 +71,8 @@ def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str `giant.data.transforms.build_cond_features` — from either a v0.2 checkpoint's flat `model_config["conditioning"]` (one shared string, same for both axes) or a new-format one (independent - `model_config["conditioning"]["particle"/"material"]["type"]` — - docs/v0.3.0-design.md §3.1: the two axes may differ). Mirrors + `model_config["conditioning"]["particle"/"material"]["type"]` — the two + axes may differ). Mirrors `giant.cli._conditioning_axes`.""" raw = model_cfg.get("conditioning", default) if isinstance(raw, dict): diff --git a/giant/analysis/type_embedding_distance.py b/giant/analysis/type_embedding_distance.py index adb39c6..8304a2a 100644 --- a/giant/analysis/type_embedding_distance.py +++ b/giant/analysis/type_embedding_distance.py @@ -1,4 +1,4 @@ -"""Secondary-type embedding-distance diagnostic (docs/v0.3.0-design.md §11.3). +"""Secondary-type embedding-distance diagnostic. Unlike every other diagnostic in this package, the data isn't derivable from a rollout/reference parquet at all — it's the L1 distance between each diff --git a/giant/cli.py b/giant/cli.py index dcd2590..6a1170c 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -78,9 +78,8 @@ def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str `giant.data.transforms.build_cond_features`/`build_features` — from either a v0.2 checkpoint's flat `model_config["conditioning"]` (one shared string, same for both axes) or a new-format one (independent - `model_config["conditioning"]["particle"/"material"]["type"]` — - docs/v0.3.0-design.md §3.1: the two axes are configured independently and - may differ).""" + `model_config["conditioning"]["particle"/"material"]["type"]` — the two + axes are configured independently and may differ).""" raw = model_cfg.get("conditioning", default) if isinstance(raw, dict): return ( @@ -390,7 +389,7 @@ def train( typer.Option( "--stage1-generator", help="Stage 1's generative objective — overrides --mode for stage 1 " - "only (see docs/v0.3.0-design.md decision 7)", + "only", ), ] = None, stage1_hidden_dim: Annotated[ @@ -725,8 +724,7 @@ def train( # --hidden-dim/--n-blocks/--dropout are stage-1-only shorthands kept for # backward compatibility (they predate stage2_model having its own # flags); --stage1-*/--stage2-* below are the explicit, discoverable - # per-stage flags docs/v0.3.0-design.md decision 7 calls for, and take - # precedence when both are given. + # per-stage flags, and take precedence when both are given. cli_stage1_model: dict[str, object] = { k: v for k, v in { @@ -1360,7 +1358,7 @@ def predict( ) if coord == Coord.global_: - # A fresh v0.3.0 Stage1Model owns no n_sec_head (decision 1) — + # A fresh v0.3.0 Stage1Model owns no n_sec_head — # sample_stage1 returns n_sec_pred=None then, so ask stage 2. n_sec_pred = resolve_n_sec( model, sec_decoder, cc, ck, stage1_norm, n_sec_pred @@ -1764,7 +1762,7 @@ def rollout( writer = pq.ParquetWriter(out, table.schema) writer.write_table(table) - # Only meaningful under particle_type.target="embedding" (§11.3) — a + # Only meaningful under particle_type.target="embedding" — a # no-op collector otherwise, cheaper than branching the call itself. l1_dist_collector = L1DistCollector() @@ -1820,7 +1818,7 @@ def rollout( "rollout_seed": seed, "n_rows": summary["n_rows"], "termination_reason_counts": summary["termination_reason_counts"], - # §11.3 diagnostic — only present under + # Diagnostic — only present under # stage2_model.particle_type.target="embedding"; omitted (not # written as null) otherwise, so giant.analysis can tell "not # applicable to this checkpoint" apart from "collector empty". diff --git a/giant/config.py b/giant/config.py index 95fc1e6..c779969 100644 --- a/giant/config.py +++ b/giant/config.py @@ -325,8 +325,7 @@ def auto_device() -> torch.device: # batch_size * hidden_dim * n_blocks (the ResBlock stack dominates), so this # is a rough estimate rather than a guaranteed bound. # NOTE: not yet recalibrated for the v0.3.0 autoregressive stage-2 trunk — -# see docs/v0.3.0-design.md §9/§11.3, deliberately last in the implementation -# order. +# deliberately last in the implementation order. _REF_BYTES = 7683 * 1024**2 _REF_BATCH_SIZE = 29696 _REF_HIDDEN_DIM = 1024 @@ -528,10 +527,9 @@ def migrate_config(cfg: dict) -> dict: Called on every config.toml load (see merge_cli_overrides) so old training configs on disk keep working under new code without hand- - editing (decision 3, docs/v0.3.0-design.md §4). `[meta].config_version == - CONFIG_VERSION` marks a dict as already-v0.3; its absence is read as - "this is v0.2" (the design doc's stated rule), so an already-migrated - dict is returned unchanged (deep-copied). + editing. `[meta].config_version == CONFIG_VERSION` marks a dict as + already-v0.3; its absence is read as "this is v0.2", so an + already-migrated dict is returned unchanged (deep-copied). Only keys actually present in `cfg` are translated — `cfg` may be a partial file (e.g. `[train]\\nepochs = 5\\n` with no [model] section at @@ -543,7 +541,7 @@ def migrate_config(cfg: dict) -> dict: to set. Operates on the config.toml shape. A checkpoint's `model_config` dict - (which additionally carries n_sec_head ownership, §4.1, and needs + (which additionally carries n_sec_head ownership and needs `network.build_models`'s cooperation) is a separate migration surface, deferred to the network.py refactor. """ @@ -729,7 +727,7 @@ def validate_config(cfg: dict) -> None: raise ValueError( "stage2_model.n_sec.mode = 'truth' is invalid for a " "rollout-capable checkpoint (both stage1_model.active and " - "stage2_model.active = true — see docs/v0.3.0-design.md §9): " + "stage2_model.active = true): " "'truth' takes n_sec from ground truth, which giant rollout " "doesn't have. 'truth' is for standalone stage-2 evaluation " "only — set stage1_model.active = false for that, or use " diff --git a/giant/data/dataset.py b/giant/data/dataset.py index 6f5f216..13c78fc 100644 --- a/giant/data/dataset.py +++ b/giant/data/dataset.py @@ -44,7 +44,6 @@ class StreamingStepsDataset(IterableDataset): where: cond_cont: (B, COND_DIM) float32 cond_cat: (B, 2/3/4) int64 — width 2 unless conditioning="onehot" - (see docs/v0.3.0-design.md decision 4) target_s1: (B, 9) float32 — normalised Stage-1 primary target n_sec: (B,) int64 — true secondary count per step sec_cont: (B, k_max, SEC_SLOT_DIM) float32 — [stick_logit, @@ -60,7 +59,7 @@ class StreamingStepsDataset(IterableDataset): ("onehot", "embedding"); zeros (unused) otherwise `k_max` (constructor arg, default the module constant) should match - `stage2_model.k_max` (docs/v0.3.0-design.md §8) — it sets the padded + `stage2_model.k_max` — it sets the padded width of `sec_cont`/`sec_type_idx` above. """ diff --git a/giant/data/loader.py b/giant/data/loader.py index e5a77bf..43a1a44 100644 --- a/giant/data/loader.py +++ b/giant/data/loader.py @@ -176,7 +176,7 @@ def iter_file_chunks( """Yield one parquet row-group at a time so a large file never fully loads. `k_max` sets the padded width of the sec_*_list columns (should match - `stage2_model.k_max` — see docs/v0.3.0-design.md §8); defaults to the + `stage2_model.k_max`); defaults to the module constant for callers that don't care (e.g. Stage-1-only reads).""" pf = pq.ParquetFile(path) for i in range(pf.num_row_groups): @@ -277,8 +277,7 @@ def _topn_plus_other_map(counts: dict, n_classes: int) -> tuple[dict, dict]: Returns `(class_map, other_members)` — `other_members` is `{key: count}` for every key bucketed into "other" (the empirical within-bucket - distribution, for `other_policy = "sample"` at rollout — see - docs/v0.3.0-design.md §8). + distribution, for `other_policy = "sample"` at rollout). """ ranked = sorted(counts, key=lambda k: counts[k], reverse=True) keep = ranked[: max(n_classes - 1, 0)] @@ -324,7 +323,7 @@ def build_topn_map_from_files( `_topn_plus_other_map`), generalized over the source column and key type. Used for the material axis (`column="material"`, `cast=str`, matching - `mat_map`'s key type) — see docs/v0.3.0-design.md §8. The PDG axis uses + `mat_map`'s key type). The PDG axis uses `build_pdg_topn_map_from_files` instead (it needs to pool two columns, which this single-column form can't express). Also records `other_members` (the empirical within-"other" distribution), needed @@ -341,8 +340,8 @@ def build_pdg_topn_map_from_files(files: list[Path], n_classes: int) -> TopNMap: plays in this dataset: a step's own primary particle (`pdg` column) and an emitted secondary's species (`sec_pdg_list`, exploded) — shared by `conditioning.particle.type = "onehot"` and - `stage2_model.particle_type.target = "onehot"` (docs/v0.3.0-design.md - §8). Pooling both is what keeps a species that's common as a secondary + `stage2_model.particle_type.target = "onehot"`. Pooling both is what + keeps a species that's common as a secondary but rare as a primary (or vice versa) from being pushed into "other" just because one role's count alone looks small — the meeting's failure mode (zero photon secondaries, hallucinated antineutrinos) was diff --git a/giant/data/setup_cache.py b/giant/data/setup_cache.py index ce38def..a0a3981 100644 --- a/giant/data/setup_cache.py +++ b/giant/data/setup_cache.py @@ -104,7 +104,7 @@ def normalizer_key( ) -> str: # .6g avoids float-repr drift (e.g. 0.1 vs 0.10000000000000002) causing # spurious cache misses between runs with the "same" val_fraction. The two - # conditioning axes are independent (docs/v0.3.0-design.md §3.1) and both + # conditioning axes are independent and both # affect which cond_cont columns are computed for real vs. zero-filled # (giant.data.transforms._physical_cond_columns), so both must be part of # the key or two mixed-axis runs could collide on the same cache entry. @@ -114,7 +114,7 @@ def normalizer_key( ) -# Top-N-map axes (docs/v0.3.0-design.md §8): "pdg" keys match pdg_map's int +# Top-N-map axes: "pdg" keys match pdg_map's int # keys (shared by conditioning.particle.type="onehot" and # stage2_model.particle_type.target="onehot" — one map for both), "material" # keys match mat_map's str keys. @@ -190,7 +190,7 @@ class SetupCache: proc_maps: dict[int, dict[str, int]] = field(default_factory=dict) normalizers: dict[str, NormalizerEntry] = field(default_factory=dict) topn_maps: dict[str, TopNMap] = field(default_factory=dict) - """Keyed by `topn_key(axis, n_classes)` — see docs/v0.3.0-design.md §8.""" + """Keyed by `topn_key(axis, n_classes)`.""" @classmethod def empty(cls, files: list[Path]) -> "SetupCache": diff --git a/giant/data/transforms.py b/giant/data/transforms.py index 555ed22..bfe7234 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -561,9 +561,9 @@ def encode_secondary_type_idx( `class_map` is either a top-N-plus-other map's `class_map` (`stage2_model.particle_type.target = "onehot"`, see `giant.data.loader.build_pdg_topn_map_from_files`) or the dense `pdg_map` - (`target = "embedding"`). Not used at all for `target = "physical"` - (see docs/v0.3.0-design.md decision 1) — that target keeps using - `encode_secondaries`'s (log_mass, charge) columns unchanged. + (`target = "embedding"`). Not used at all for `target = "physical"` — + that target keeps using `encode_secondaries`'s (log_mass, charge) + columns unchanged. Padding slots get index 0 (their looked-up value is discarded downstream by the `sec_valid`/`n_sec` mask regardless, so any in-vocabulary dummy @@ -599,7 +599,7 @@ def decode_secondary_cont( stick-breaking energy split and local->world direction — generator/ `particle_type.target`-independent, since every target (`"physical"`, `"onehot"`, `"embedding"`) shares the same `CONT_SLOT_DIM`-wide - (stick_logit, dir) prefix (docs/v0.3.0-design.md §6.1) and differs only + (stick_logit, dir) prefix and differs only in what follows it. `decode_secondaries` (target="physical") is the original all-in-one form built on top of this; `target` in `("onehot", "embedding")` decodes their type slice separately via @@ -727,9 +727,8 @@ def _physical_cond_columns( ) -> np.ndarray: """(N, PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM) physical conditioning columns. - The particle and material blocks are gated independently - (docs/v0.3.0-design.md §3.1: "configured independently and may mix - freely — e.g. material `physical` with particle `embedding`"), so e.g. + The particle and material blocks are gated independently and may mix + freely — e.g. material `physical` with particle `embedding` — so e.g. `particle_conditioning="embedding"` + `material_conditioning="physical"` zero-fills only the particle columns and computes the material ones for real. @@ -802,16 +801,15 @@ def build_cond_features( ) -> tuple[np.ndarray, np.ndarray]: """Build conditioning arrays only — no target, no post-step variables. - `particle_conditioning`/`material_conditioning` are independent - (docs/v0.3.0-design.md §3.1) — e.g. `particle_conditioning="embedding"` + + `particle_conditioning`/`material_conditioning` are independent — + e.g. `particle_conditioning="embedding"` + `material_conditioning="physical"` is a valid mix. `pdg_topn_map`/`mat_topn_map` (a top-N-plus-other `class_map`, see `giant.data.loader.build_topn_map_from_files`) append extra `cond_cat` - columns read by `ConditionEncoder`'s `"onehot"` mode - (docs/v0.3.0-design.md decision 4): pdg topN index at column 2 (iff - `pdg_topn_map` given), material topN index at column 3 (iff - `mat_topn_map` given, after column 2 if both are). Only ever given when + columns read by `ConditionEncoder`'s `"onehot"` mode: pdg topN index at + column 2 (iff `pdg_topn_map` given), material topN index at column 3 + (iff `mat_topn_map` given, after column 2 if both are). Only ever given when the corresponding axis is `"onehot"`; `cond_cat` stays `(N, 2)` otherwise. """ cond_cont = np.column_stack( @@ -936,9 +934,8 @@ def build_features( the secondary's real physical identity (from its ground-truth PDG code), a fixed regression target, not a learned/snapped one. Always computed the same way regardless of - `stage2_model.particle_type.target` (docs/v0.3.0-design.md - decision 1/3) — only actually used downstream under `target = - "physical"`. + `stage2_model.particle_type.target` — only actually used + downstream under `target = "physical"`. proc_idx: (N,) integer process-class label (ProcessRouter supervision only — never conditioning). Zeros when `proc_map` is None or the loaded data has no "process" column (e.g. pre-conversion parquet files). @@ -966,7 +963,7 @@ def build_features( dense `pdg_map` for `target = "embedding"` (pass `pdg_map` itself). `None` for `target = "physical"`. - k_max: should match `stage2_model.k_max` (docs/v0.3.0-design.md §8) — + k_max: should match `stage2_model.k_max` — overridden internally by `data["sec_E_list"]`'s own padded width when present (the loader already padded it to some k_max; that width is authoritative), so this only actually matters when secondary list @@ -1015,7 +1012,7 @@ def build_features( cat_cols.append(_vectorized_map_lookup(data["pdg"], pdg_topn_map)) if mat_topn_map is not None: cat_cols.append(_vectorized_map_lookup(data["material"], mat_topn_map)) - cond_cat = np.column_stack(cat_cols) # (N, 2/3/4) — see decision 4 + cond_cat = np.column_stack(cat_cols) # (N, 2/3/4) n_sec_raw = data["n_sec"].astype( np.int64 @@ -1029,7 +1026,7 @@ def build_features( # The loader already padded sec_*_list to some k_max (see # giant.data.loader.iter_file_chunks); that padded width is # authoritative over whatever this call happened to pass in, so the - # two can never drift apart (docs/v0.3.0-design.md §8). + # two can never drift apart. k_max = sec_E_list.shape[1] # Clamp the classification label to k_max: the head only has k_max+1 diff --git a/giant/model/network.py b/giant/model/network.py index ce91890..8aeadfb 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -21,7 +21,7 @@ from giant.constants import ( ) # --------------------------------------------------------------------------- -# Building blocks (docs/v0.3.0-design.md §5.2/§5.3) +# Building blocks # --------------------------------------------------------------------------- @@ -47,7 +47,7 @@ def cat_col_layout( particle_type: str, material_type: str ) -> tuple[int | None, int | None]: """`cond_cat` column indices for each axis's top-N-onehot index, or - `None` if that axis isn't `"onehot"` (docs/v0.3.0-design.md decision 4). + `None` if that axis isn't `"onehot"`. Columns 0/1 are always the dense pdg/material vocab index. The particle top-N column (if any) comes next, then the material top-N column (if @@ -92,9 +92,9 @@ class ConditionEncoder(nn.Module): """Fuses continuous conditioning with particle/material identity. The particle and material axes are configured independently - (`particle_cfg`/`material_cfg`, each `{"type", "emb_dim", "n_layers"}` — - see docs/v0.3.0-design.md §3.1) and may mix freely, e.g. material - "physical" with particle "embedding". Three modes per axis: + (`particle_cfg`/`material_cfg`, each `{"type", "emb_dim", "n_layers"}`) + and may mix freely, e.g. material "physical" with particle "embedding". + Three modes per axis: - "embedding": a learned `nn.Embedding` lookup, indexed by `cond_cat`'s dense training-vocab index. Memorizes the training menu. - "physical": an `n_layers`-deep MLP over the axis's raw physical @@ -104,8 +104,7 @@ class ConditionEncoder(nn.Module): - "onehot": a fixed, unlearned one-hot vector over a top-N-plus-other class map (`giant.data.loader.build_topn_map_from_files`/ `build_pdg_topn_map_from_files`), read from `cond_cat`'s extra - top-N-index column(s) — see `_cat_col_layout` and - docs/v0.3.0-design.md decision 4. + top-N-index column(s) — see `_cat_col_layout`. """ def __init__( @@ -193,7 +192,7 @@ class ContextAdapter(nn.Module): fixed-width context vector for a downstream stage's conditioning — `stage2_model.context_dim`. Was `SecondaryConditionEncoder.stage1_proj` (+ its `tanh`) in v0.2; pulled out as its own module in v0.3.0 since - `SecondaryConditionEncoder` as a wrapper class disappears (design doc §5.2).""" + `SecondaryConditionEncoder` as a wrapper class disappears.""" def __init__(self, in_dim: int, context_dim: int) -> None: super().__init__() @@ -223,7 +222,7 @@ class ResBlock(nn.Module): # --------------------------------------------------------------------------- -# Routers — carried over unchanged from v0.2 (docs/v0.3.0-design.md §5.3) +# Routers — carried over unchanged from v0.2 # --------------------------------------------------------------------------- @@ -615,7 +614,7 @@ def _build_router_from_cfg( # --------------------------------------------------------------------------- -# Trunks (docs/v0.3.0-design.md §5.2 (b)) +# Trunks # --------------------------------------------------------------------------- @@ -783,7 +782,7 @@ def build_trunk( # --------------------------------------------------------------------------- -# History encoders — stage-2 autoregressive only (docs/v0.3.0-design.md §6.2) +# History encoders — stage-2 autoregressive only # --------------------------------------------------------------------------- @@ -791,8 +790,8 @@ class HistoryEncoder(nn.Module): """Interface for stage-2 autoregressive per-token history summaries: `forward(feat, has_prev) -> (B, K, out_dim)`, a single parallel pass over a full (teacher-forced) token sequence — used by training. `MarkovHistory` - and `AttentionHistory` (docs/v0.3.0-design.md §6.2) are the two - implementations. Inference (`giant/sample.py`) generates one token at a + and `AttentionHistory` are the two implementations. Inference + (`giant/sample.py`) generates one token at a time and cannot afford `forward`'s per-step cost to be O(K) (attention would then be O(K^2) over a rollout's k_max loop); encoders that need incremental state for that path additionally implement `init_cache`/ @@ -806,13 +805,13 @@ class HistoryEncoder(nn.Module): class MarkovHistory(HistoryEncoder): """Summarizes the previous secondary's own `(energy_fraction, direction, - type_representation)` through one small MLP — the "markov" history - (docs/v0.3.0-design.md §6.2): token i+1 only ever sees token i plus the - running scalars (`remaining_frac`/`slot_idx`, fused in separately by + type_representation)` through one small MLP — the "markov" history: + token i+1 only ever sees token i plus the running scalars + (`remaining_frac`/`slot_idx`, fused in separately by `Stage2Autoregressive._token_cond`), not the full prefix. At slot 0 (`has_prev` False) substitutes a learned start vector rather - than zeros — a reasonable default, not specified by the design doc. + than zeros — a reasonable default. """ def __init__(self, in_dim: int, out_dim: int) -> None: @@ -879,9 +878,9 @@ class _CausalAttnBlock(nn.Module): class AttentionHistory(HistoryEncoder): - """Causal self-attention over the emitted-token prefix - (docs/v0.3.0-design.md §6.2) — the more expressive alternative to - `MarkovHistory`'s fixed previous-token-only summary. `feat`/`has_prev` + """Causal self-attention over the emitted-token prefix — the more + expressive alternative to `MarkovHistory`'s fixed previous-token-only + summary. `feat`/`has_prev` follow the same shifted-by-one convention `MarkovHistory` and `Stage2Autoregressive._token_cond` use: `feat[:, i]` is token `i - 1`'s own `(energy_fraction, direction, type_representation)`, with a learned @@ -893,9 +892,9 @@ class AttentionHistory(HistoryEncoder): `forward` is the parallel training path (one pass over the whole teacher-forced sequence); `init_cache`/`step` are the incremental inference path `giant/sample.py` uses, one new token per call, to avoid - re-encoding the whole prefix from scratch every slot (docs/v0.3.0-design.md - §10's "KV cache" note) — `step` must be called exactly once per slot (its - cache-extension is not idempotent), so a slot's output must be reused for + re-encoding the whole prefix from scratch every slot — `step` must be + called exactly once per slot (its cache-extension is not idempotent), + so a slot's output must be reused for every model call within that slot (`forward`'s ODE substeps, or a separate `predict_type` call) rather than re-derived — see `Stage2Autoregressive.history_step`. @@ -947,7 +946,7 @@ class AttentionHistory(HistoryEncoder): # --------------------------------------------------------------------------- -# Stage models (docs/v0.3.0-design.md §5.3) +# Stage models # --------------------------------------------------------------------------- @@ -955,7 +954,7 @@ def stage2_type_dim(particle_type_cfg: dict, emb_dim: int) -> int: """Width of a single secondary slot's type slice — `PARTICLE_PHYS_DIM` (log_mass, charge) for `target = "physical"`, else `emb_dim` (both `"onehot"` class logits and `"embedding"` vectors are - `conditioning.particle.emb_dim` wide — docs/v0.3.0-design.md §3.3).""" + `conditioning.particle.emb_dim` wide).""" target = particle_type_cfg.get("target", "physical") return PARTICLE_PHYS_DIM if target == "physical" else emb_dim @@ -963,15 +962,15 @@ def stage2_type_dim(particle_type_cfg: dict, emb_dim: int) -> int: def stage2_trunk_sec_dim( particle_type_cfg: dict, generator: str, k_max: int, emb_dim: int ) -> int: - """`Stage2OneShot`'s trunk output width (docs/v0.3.0-design.md decision 2). + """`Stage2OneShot`'s trunk output width. - `target = "physical"` is untouched from v0.2/today (decision 1): + `target = "physical"` is untouched from v0.2/today: `k_max * SEC_SLOT_DIM`, the type slice folded into the same flow-matched/WGAN vector as the continuous stick/dir slots. `target` in `("onehot", "embedding")`: under `generator == "wgan"` the type slice is still folded in (adversarial for onehot via ST-Gumbel, - already-continuous for embedding — §2.1), just `emb_dim` wide instead of + already-continuous for embedding), just `emb_dim` wide instead of `PARTICLE_PHYS_DIM` wide: `k_max * (CONT_SLOT_DIM + emb_dim)`. Under `generator in ("flow", "ddpm")` the type slice isn't part of this vector at all — it's `Stage2OneShot.type_head`'s job instead — so the trunk @@ -986,14 +985,14 @@ def stage2_trunk_sec_dim( class Stage1Model(nn.Module): - """Predicts the 9D primary post-step vector. No `n_sec_head` — decision 1 - (docs/v0.3.0-design.md §2) moves it to stage 2, except for a migrated - v0.2 checkpoint (`n_sec_head_k_max` given), where it stays attached here + """Predicts the 9D primary post-step vector. No `n_sec_head` — fresh runs + move it to stage 2, except for a migrated v0.2 checkpoint + (`n_sec_head_k_max` given), where it stays attached here since that's where its weights live and what conditioning it was trained against (see `_migrate_legacy_model_config`). `cond_enc`, if given, is used in place of building a fresh - `ConditionEncoder` — `conditioning.share_stages = true` (§3.1): `build_models` + `ConditionEncoder` — `conditioning.share_stages = true`: `build_models` constructs one shared instance and passes it to both stages, halving the conditioning parameter count and forcing a common representation.""" @@ -1060,11 +1059,11 @@ class Stage1Model(nn.Module): ) -> torch.Tensor: """Return n_sec logits (B, K_MAX+1) from conditioning alone. Only valid on a migrated v0.2 checkpoint's Stage1Model — fresh v0.3.0 - configs predict n_sec from Stage2OneShot instead (decision 1).""" + configs predict n_sec from Stage2OneShot instead.""" if self.n_sec_head is None: raise RuntimeError( "this Stage1Model has no n_sec_head — n_sec now lives on " - "stage 2 by default (decision 1); this method only exists " + "stage 2 by default; this method only exists " "for a migrated v0.2 checkpoint (legacy_owner='stage1')" ) c_emb = self.cond_enc(cond_cont, cond_cat) @@ -1073,17 +1072,16 @@ class Stage1Model(nn.Module): class Stage2OneShot(nn.Module): """Predicts all `k_max` secondary slots simultaneously — v0.2 behaviour, - reproduced exactly (see docs/v0.3.0-design.md §12 step 2 acceptance - criterion; `decoder = "autoregressive"` is `Stage2Autoregressive`, + reproduced exactly (`decoder = "autoregressive"` is `Stage2Autoregressive`, step 4/5, not implemented yet). - Owns `n_sec_head` by default (decision 1) unless `build_n_sec_head=False` + Owns `n_sec_head` by default unless `build_n_sec_head=False` (a migrated v0.2 checkpoint, whose n_sec_head instead attaches to Stage1Model — see `_migrate_legacy_model_config`). `particle_type_cfg["target"]` (default `"physical"`) selects the - secondary-type mechanism (docs/v0.3.0-design.md decision 2): - `"physical"` keeps the type slice folded into the trunk's own + secondary-type mechanism: `"physical"` keeps the type slice folded into + the trunk's own flow-matched/WGAN output, unchanged from v0.2 (`sec_dim` — computed by the caller via `stage2_trunk_sec_dim` — already reflects this). Under `"onehot"`/`"embedding"` with `generator in ("flow", "ddpm")`, the type @@ -1225,8 +1223,8 @@ class Stage2OneShot(nn.Module): class Stage2Autoregressive(nn.Module): - """Emits secondaries one at a time in descending-energy order - (docs/v0.3.0-design.md §6), instead of `Stage2OneShot`'s simultaneous + """Emits secondaries one at a time in descending-energy order, instead + of `Stage2OneShot`'s simultaneous k_max-slot prediction. `history` selects `MarkovHistory` or `AttentionHistory` (`attn_n_heads`/`attn_n_layers`, attention only). `teacher_forcing` handling lives entirely in the trainer @@ -1555,7 +1553,7 @@ class CriticModel(nn.Module): # --------------------------------------------------------------------------- -# v0.2 -> v0.3 checkpoint migration (docs/v0.3.0-design.md §4.1, §4.3) +# v0.2 -> v0.3 checkpoint migration # --------------------------------------------------------------------------- @@ -1568,14 +1566,14 @@ def _migrate_legacy_model_config(model_config: dict) -> dict: Sets `stage2_model.n_sec.legacy_owner = "stage1"` so the n_sec_head weights a v0.2 checkpoint carries on its Stage-1 module keep loading - there (design doc §4.1) instead of the new default location + there instead of the new default location (`Stage2OneShot`) — the n_sec head was trained against Stage 1's own `ConditionEncoder` output, so it has to stay attached to Stage 1's module, not just be labeled as such. Only the monolithic (non-routed) trunk shape is exercised by the step-2 - migration test (docs/v0.3.0-design.md §4.3); a routed v0.2 checkpoint - still builds correctly here (the router config passes through), but its + migration test; a routed v0.2 checkpoint still builds correctly here + (the router config passes through), but its state dict isn't covered by `migrate_legacy_state_dict` below. """ m = model_config @@ -1599,7 +1597,7 @@ def _migrate_legacy_model_config(model_config: dict) -> dict: "hidden_dim/n_res_blocks), so this checkpoint's routed experts " "have a different width/depth than the monolith — silently " "dropping these keys would resize the experts instead of " - "refusing (docs/v0.3.0-design.md §4.2). This checkpoint can " + "refusing. This checkpoint can " "only be loaded by v0.2 code." ) router_cfg.setdefault("enabled", False) @@ -1651,8 +1649,7 @@ def migrate_legacy_state_dict( `(Stage1Model, Stage2OneShot)` module structure produced by `build_models(_migrate_legacy_model_config(model_config))`. - Only the monolithic (non-routed) trunk shape is handled — see - docs/v0.3.0-design.md §4.3's migration test scope. + Only the monolithic (non-routed) trunk shape is handled. """ def _trunk_prefix(k: str) -> str: @@ -1682,7 +1679,7 @@ def migrate_legacy_state_dict( # --------------------------------------------------------------------------- -# Factories (docs/v0.3.0-design.md §5.4) +# Factories # --------------------------------------------------------------------------- @@ -1698,7 +1695,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: (v0.2's actual — probably accidental — behaviour: two routers built from one config with no semantic relationship between them). - `conditioning.share_stages = true` (§3.1) builds one `ConditionEncoder` + `conditioning.share_stages = true` builds one `ConditionEncoder` instance here and passes it to both stages (`Stage1Model`/`Stage2OneShot`/ `Stage2Autoregressive`'s `cond_enc` param), instead of each stage building its own — halving the conditioning parameter count and forcing a diff --git a/giant/model/schedule.py b/giant/model/schedule.py index 27d8282..b8b3891 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -90,8 +90,8 @@ def flow_matching_loss_secondary( "physical"`'s width and the only case this function handled before v0.3.0 step 4. `0` means no type slice is in `x1` at all (`target` in `("onehot", "embedding")` under `generator in ("flow", "ddpm")` — - see docs/v0.3.0-design.md decision 2, `Stage2OneShot.type_head` - handles the type loss separately in that case). + `Stage2OneShot.type_head` handles the type loss separately in that + case). Only valid-slot dimensions contribute to the loss; padded slots are zeroed before averaging, so the loss is not diluted by empty slots. @@ -156,7 +156,7 @@ def flow_matching_loss_secondary_ar( model call signature differs enough (four extra per-token conditioning tensors) that merging would need an awkward shape-flag + closure. - Under teacher forcing (docs/v0.3.0-design.md §6.2 point 3) this is still a + Under teacher forcing this is still a single parallel pass over all K_MAX tokens — `x1`/`history_feat`/etc. are already built from ground truth for every slot by the caller (`giant.training.stage2_inputs._assemble_stage2_ar_inputs`/`_assemble_stage2_ar_target`). diff --git a/giant/particles.py b/giant/particles.py index 7e2d5ac..c6be4e9 100644 --- a/giant/particles.py +++ b/giant/particles.py @@ -8,8 +8,8 @@ nuclide table doesn't cover — confirmed necessary for ~32% of the nuclear code actually present in the multi-material dataset (`0932fb02-f2ce-43ca-a4ef-60a2b1221bbc.parquet`). -Also holds the v0.3.0 stage-2 categorical-type rollout decode (§3.3/§8/§11.3 -of docs/v0.3.0-design.md): `decode_topn_class`/`decode_embedding_nearest` turn +Also holds the v0.3.0 stage-2 categorical-type rollout decode: +`decode_topn_class`/`decode_embedding_nearest` turn `Stage2Autoregressive`/`Stage2OneShot`'s `"onehot"`/`"embedding"` type predictions back into concrete PDG codes, the one place a secondary's categorical/continuous type representation is ever discretized (its @@ -145,8 +145,8 @@ def decode_topn_class( rng: np.random.Generator | None = None, ) -> np.ndarray: """`conditioning.particle.type` / `stage2_model.particle_type.target = - "onehot"` inference decode (docs/v0.3.0-design.md §3.3): per-row top-N - class index -> concrete PDG code. + "onehot"` inference decode: per-row top-N class index -> concrete PDG + code. class_idx: int array, any shape, values in `[0, n_classes)`. topn_map: the `TopNMap` (`giant.data.loader.build_pdg_topn_map_from_files`) @@ -205,9 +205,9 @@ def decode_embedding_nearest( emb_weight: np.ndarray, idx_to_pdg: dict[int, int], ) -> tuple[np.ndarray, np.ndarray]: - """`stage2_model.particle_type.target = "embedding"` inference decode - (docs/v0.3.0-design.md §3.3): L1-nearest row of the conditioning's own - particle embedding table, since a generative model's continuous output + """`stage2_model.particle_type.target = "embedding"` inference decode: + L1-nearest row of the conditioning's own particle embedding table, since + a generative model's continuous output essentially never lands within float tolerance of a table row (the exact- match form is only valid as a round-trip test assertion, never here). @@ -220,9 +220,9 @@ def decode_embedding_nearest( idx_to_pdg: `invert_dense_map(pdg_map)` — embedding row index -> PDG. Returns `(pdg, l1_dist)`, both shaped like `vectors.shape[:-1]`. `l1_dist` - is the §11.3 diagnostic: a heavy tail means the decoder is emitting - vectors off the embedding manifold, the direct analogue of the species- - collapse symptom this redesign exists to fix. + is a diagnostic: a heavy tail means the decoder is emitting vectors off + the embedding manifold, the direct analogue of the species-collapse + symptom this redesign exists to fix. """ emb_dim = vectors.shape[-1] flat = np.asarray(vectors, dtype=np.float64).reshape(-1, emb_dim) diff --git a/giant/pipeline.py b/giant/pipeline.py index c427a1b..843140d 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -68,7 +68,7 @@ def _seed_energy_router( """Mutate `router_cfg["centers_init"]` in place from real data quantiles, when this stage's router is an enabled EnergyRouter. Shared by both stages' router configs — each seeded independently, since v0.3.0 stages - may have entirely different router configs (see docs/v0.3.0-design.md).""" + may have entirely different router configs.""" active = router_cfg.get("enabled") and router_cfg.get("type") == "energy" if not active: return @@ -107,7 +107,7 @@ def run_setup_stage( `cfg` is the full merged v0.3 config (`conditioning`/`stage1_model`/ `stage2_model`), already passed through `giant.config.validate_config`. `conditioning.particle.type` and `conditioning.material.type` are - independent (docs/v0.3.0-design.md §3.1) and may differ. + independent and may differ. Reads from and writes to the `giant.data.setup_cache` sidecar when `cache_setup` is set (`rebuild_setup_cache` ignores — but still @@ -197,8 +197,8 @@ def run_setup_stage( if cache is not None: cache.proc_maps[n_experts] = proc_map - # Top-N-plus-other maps for onehot conditioning/type axes - # (docs/v0.3.0-design.md §8). The PDG axis is shared by + # Top-N-plus-other maps for onehot conditioning/type axes. + # The PDG axis is shared by # conditioning.particle.type="onehot" and # stage2_model.particle_type.target="onehot" (both key off # conditioning.particle.emb_dim), so at most one PDG scan is needed even @@ -403,11 +403,11 @@ def run_train_job( setup.n_train_steps, ) - # cond_cat's onehot columns (docs/v0.3.0-design.md decision 4) are - # present per-axis, independently, under that axis's own - # conditioning.{particle,material}.type == "onehot" (§3.1: the two axes - # may mix freely). run_setup_stage builds each map whenever its own axis - # is "onehot" (see its own particle_cfg["type"]/material_cfg["type"] + # cond_cat's onehot columns are present per-axis, independently, under + # that axis's own conditioning.{particle,material}.type == "onehot" + # (the two axes may mix freely). run_setup_stage builds each map + # whenever its own axis is "onehot" (see its own + # particle_cfg["type"]/material_cfg["type"] # checks), so they're guaranteed non-None here — asserted, not just # assumed, so a future wiring bug fails loudly instead of silently # dropping the onehot columns. @@ -422,7 +422,7 @@ def run_train_job( # The secondary type-index map depends on stage2_model.particle_type.target, # independently of conditioning's own onehot/embedding choice above - # (docs/v0.3.0-design.md §3.3 — physical stays untouched/None). + # (physical stays untouched/None). particle_type_target = ( cfg["stage2_model"].get("particle_type", {}).get("target", "physical") ) diff --git a/giant/rollout.py b/giant/rollout.py index c38edd4..bb6b9fd 100644 --- a/giant/rollout.py +++ b/giant/rollout.py @@ -53,14 +53,14 @@ if TYPE_CHECKING: class L1DistCollector: - """Accumulates the §11.3 L1-distance diagnostic across a whole rollout + """Accumulates the L1-distance diagnostic across a whole rollout run: the L1 distance between each emitted secondary's raw predicted embedding vector and the nearest table row it snapped to (only meaningful under `particle_type.target = "embedding"` — `giant.particles.decode_embedding_nearest`). A heavy tail means the decoder is emitting vectors off the embedding manifold — the direct analogue of the species-collapse symptom the v0.3.0 redesign exists to - fix (docs/v0.3.0-design.md §11.3). + fix. Not folded into `rollout()`'s own return value (which is shape-typed as step records, see `_RECORD_KEYS`/`RolloutSummary`) — passed in and read @@ -124,8 +124,7 @@ def decode_secondary_identity( np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray | None ]: """Decode Stage 2's raw (sec_cont, sec_type) output into physical - secondary attributes, branching on `sec_decoder.particle_type_cfg` - (docs/v0.3.0-design.md §3.3): + secondary attributes, branching on `sec_decoder.particle_type_cfg`: - `"physical"`: unchanged v0.2 path — `sec_type` already *is* (log_mass, charge), used as the secondary's identity as-is (no snapping). @@ -138,8 +137,8 @@ def decode_secondary_identity( - `"embedding"`: `sec_type` is a raw vector in the conditioning's own embedding space — `giant.particles.decode_embedding_nearest` L1-snaps it to the nearest table row for the PDG (+ physics via - `particle_phys_array`), and also returns the L1 distance (§11.3 - diagnostic — see `giant/rollout.py`'s L1-distance accumulator). + `particle_phys_array`), and also returns the L1 distance (see this + module's `L1DistCollector`). Returns (sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, sec_type_l1_dist) — the last is `None` except under `"embedding"`. @@ -480,24 +479,22 @@ def rollout( `n_events * max_steps * avg_tracks_per_event`. There is no `mode` parameter — each stage's generative objective is read - directly off the model instance's own `generator_kind` - (docs/v0.3.0-design.md decision 2: stage 1 and stage 2 objectives are - independent, e.g. `stage1_model.generator="flow"` + - `stage2_model.generator="wgan"`), and the decoder (one-shot vs + directly off the model instance's own `generator_kind` (stage 1 and + stage 2 objectives are independent, e.g. `stage1_model.generator="flow"` + + `stage2_model.generator="wgan"`), and the decoder (one-shot vs autoregressive) is inferred from `sec_decoder`'s own class — see `sample_stage1`/`sample_stage2` (giant.sample). `pdg_topn_map`/`mat_topn_map` serve two independent purposes that happen - to share `pdg_topn_map` (docs/v0.3.0-design.md §8 — one PDG map, not - two): they're required whenever `particle_conditioning`/ - `material_conditioning` is `"onehot"` (feeds `build_cond_features`'s - extra `cond_cat` top-N columns — §3.1), and `pdg_topn_map`/`other_policy` - are additionally read under `stage2_model.particle_type.target = - "onehot"` (§3.3, secondary-species decode). `seed` seeds the - `other_policy = "sample"` draw only (torch/numpy sampling itself is - seeded by the caller, same as today). + to share `pdg_topn_map` (one PDG map, not two): they're required + whenever `particle_conditioning`/`material_conditioning` is `"onehot"` + (feeds `build_cond_features`'s extra `cond_cat` top-N columns), and + `pdg_topn_map`/`other_policy` are additionally read under + `stage2_model.particle_type.target = "onehot"` (secondary-species + decode). `seed` seeds the `other_policy = "sample"` draw only + (torch/numpy sampling itself is seeded by the caller, same as today). - `l1_dist_collector`, if given, accumulates the §11.3 embedding-distance + `l1_dist_collector`, if given, accumulates the embedding-distance diagnostic across the whole run — see `L1DistCollector`. Only populated under `particle_type.target = "embedding"`; a no-op otherwise. """ diff --git a/giant/sample.py b/giant/sample.py index e5fb65f..3683384 100644 --- a/giant/sample.py +++ b/giant/sample.py @@ -10,10 +10,9 @@ def _predict_n_sec_if_owned( model: torch.nn.Module, cond_cont: torch.Tensor, cond_cat: torch.Tensor ) -> torch.Tensor | None: """Stage-1 `n_sec_head` is only present on a migrated v0.2 checkpoint - (docs/v0.3.0-design.md decision 1 moves it to stage 2 for fresh runs — - see `Stage1Model`'s docstring). `None` here means "ask stage 2 instead", - which every caller (`giant/rollout.py`, `giant/cli.py`) must do for a - fresh checkpoint.""" + (fresh runs move it to stage 2 — see `Stage1Model`'s docstring). `None` + here means "ask stage 2 instead", which every caller (`giant/rollout.py`, + `giant/cli.py`) must do for a fresh checkpoint.""" if getattr(model, "n_sec_head", None) is None: return None logits = model.predict_n_sec(cond_cont, cond_cat) @@ -124,7 +123,7 @@ def _stage2_flat_width(sec_decoder: torch.nn.Module) -> int: (continuous + type) under `particle_type.target = "physical"` or `generator = "wgan"`, continuous-only otherwise (the type slice then comes from `predict_type` instead — see `stage2_trunk_sec_dim`'s - docstring, docs/v0.3.0-design.md decision 2).""" + docstring).""" return stage2_trunk_sec_dim( sec_decoder.particle_type_cfg, sec_decoder.generator_kind, @@ -147,9 +146,9 @@ def _decode_stage2_flat( n_sec_pred: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Reshape a flat `(B, flat_width)` `Stage2OneShot` output into per-slot - tensors, generator/`particle_type.target`-agnostic (docs/v0.3.0-design.md - decision 2/3): shared by `sample_secondaries`/`sample_secondaries_wgan`, - which differ only in how `x` was produced. + tensors, generator/`particle_type.target`-agnostic: shared by + `sample_secondaries`/`sample_secondaries_wgan`, which differ only in how + `x` was produced. Returns (sec_cont, sec_type, sec_valid): sec_cont: (B, k_max, CONT_SLOT_DIM) — [stick_logit, local_dir] @@ -241,23 +240,22 @@ def sample_secondaries_ar( n_sec_pred: torch.Tensor, steps: int = 10, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """`Stage2Autoregressive` inference loop (docs/v0.3.0-design.md §6.4): - one token at a time, in descending-energy slot order, `k_max` sequential - calls. Unlike training (teacher forcing, §6.2 point 3 — a single - parallel pass over ground-truth tokens, see - `giant.training.stage2_inputs._assemble_stage2_ar_inputs`), there is no ground truth at - inference: each token's conditioning is built free-running, from the - PREVIOUS TOKEN'S OWN just-generated output — the train/inference gap - §6.2 point 4 explicitly flags as the cost of markov history's + """`Stage2Autoregressive` inference loop: one token at a time, in + descending-energy slot order, `k_max` sequential calls. Unlike training + (teacher forcing — a single parallel pass over ground-truth tokens, see + `giant.training.stage2_inputs._assemble_stage2_ar_inputs`), there is no + ground truth at inference: each token's conditioning is built + free-running, from the PREVIOUS TOKEN'S OWN just-generated output — the + train/inference gap that is the cost of markov history's expressiveness. A `{flow,ddpm}` token costs `steps` ODE substeps; `wgan` costs one pass — - §6.4's "K sequential forwards" cost note applies per-token here, not + the "K sequential forwards" cost applies per-token here, not once, so a flow/ddpm AR run costs ~`k_max * steps` model calls per physics step. Under `history="attention"` the history encoding is computed once per - slot via `Stage2Autoregressive.history_step` (a KV-cache append, §10) + slot via `Stage2Autoregressive.history_step` (a KV-cache append) rather than re-derived by every model call inside that slot — so an ODE loop's `steps` substeps, and the separate `predict_type` call when the type slice isn't folded into the trunk output, all reuse the SAME `hist` @@ -389,9 +387,9 @@ def sample_secondaries_ar( # --------------------------------------------------------------------------- # Per-stage dispatch — shared by giant/rollout.py and giant/cli.py's # `predict` command, since both need "given a stage model, produce a -# sample" without hand-picking the sampler themselves (docs/v0.3.0-design.md -# decision 2: each stage's generative objective is independent, read off the -# model's own `generator_kind`, not a caller-supplied `mode` string). +# sample" without hand-picking the sampler themselves (each stage's +# generative objective is independent, read off the model's own +# `generator_kind`, not a caller-supplied `mode` string). # --------------------------------------------------------------------------- @@ -450,19 +448,17 @@ def resolve_n_sec( ) -> torch.Tensor: """`n_sec_pred` is already populated when `stage1_model` owns a legacy `n_sec_head` (a migrated v0.2 checkpoint — see `Stage1Model`'s - docstring); otherwise ask stage 2, which owns it by default under - decision 1 (docs/v0.3.0-design.md §2). Raises if neither stage owns a - head at all — the only way that happens is `stage2_model.n_sec.mode` - other than `"head"` (`"truth"`/`"stop_token"`), neither of which is a - valid rollout-/predict-capable checkpoint (§3.3, §9).""" + docstring); otherwise ask stage 2, which owns it by default. Raises if + neither stage owns a head at all — the only way that happens is + `stage2_model.n_sec.mode` other than `"head"` (`"truth"`/`"stop_token"`), + neither of which is a valid rollout-/predict-capable checkpoint.""" if n_sec_pred is not None: return n_sec_pred if getattr(sec_decoder, "n_sec_head", None) is None: raise RuntimeError( "checkpoint has no n_sec_head on either stage — needs " "stage2_model.n_sec.mode = 'head' (the default); 'truth' is " - "standalone-evaluation-only and 'stop_token' isn't implemented " - "(docs/v0.3.0-design.md §3.3/§9)" + "standalone-evaluation-only and 'stop_token' isn't implemented" ) logits = sec_decoder.predict_n_sec(cond_cont, cond_cat, stage1_out) return logits.argmax(dim=-1) diff --git a/giant/training/loop.py b/giant/training/loop.py index 39719cc..b2cb192 100644 --- a/giant/training/loop.py +++ b/giant/training/loop.py @@ -72,9 +72,8 @@ class _GracefulShutdown: def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs): """Runs `validate_marginals` on `trainer`'s sampling model (EMA model if present, else the raw model). `validate_marginals` itself dispatches - through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec` - (docs/v0.3.0-design.md §10), so this is generator- and - one-shot-vs-autoregressive-agnostic.""" + through `giant.sample.sample_stage1`/`sample_stage2`/`resolve_n_sec`, so + this is generator- and one-shot-vs-autoregressive-agnostic.""" model = trainer.sampling_model() return validate_marginals(model, val_loader, device=device, **kwargs) @@ -124,7 +123,7 @@ def train( wandb_log_every: int = 50, ) -> None: """Train whichever of stage1/stage2 are active, each through its own - `StageTrainer` (design doc §7). `models`/`critics` are the dicts + `StageTrainer`. `models`/`critics` are the dicts `giant.model.network.build_models`/`build_critics` return — a `None` entry means that stage is `active = false`. """ diff --git a/giant/training/stage2_inputs.py b/giant/training/stage2_inputs.py index 2508f5d..b23ac5d 100644 --- a/giant/training/stage2_inputs.py +++ b/giant/training/stage2_inputs.py @@ -4,7 +4,7 @@ Pure functions, no optimizer/model state: they turn a batch's ground-truth secondary tensors into the per-token targets and autoregressive conditioning inputs `giant.training.trainers` feeds to `Stage2OneShot` / `Stage2Autoregressive`. Split out of the trainers so the (target, generator, -decoder) width rules — the fiddliest part of docs/v0.3.0-design.md §2.1/§6 — +decoder) width rules — the fiddliest part of this codebase — live in one place and stay unit-testable on their own. """ @@ -65,8 +65,7 @@ def _assemble_stage2_ar_target( analogue of `_assemble_stage2_real` (defined below in terms of this), matching whatever width `Stage2Autoregressive`'s (or `Stage2OneShot`'s) own trunk produces for this (target, generator) combination - (`giant.model.network.stage2_trunk_sec_dim`; docs/v0.3.0-design.md - decision 2/3): + (`giant.model.network.stage2_trunk_sec_dim`): - `target = "physical"`: unchanged from v0.2 — `sec_cont` (stick_logit, dir, log_mass, charge) as-is. @@ -116,7 +115,7 @@ def _stick_fraction(sec_cont: torch.Tensor) -> torch.Tensor: def _remaining_energy_fraction(fraction: torch.Tensor) -> torch.Tensor: """(B, K_MAX) — fraction of the original e_sec budget unclaimed entering slot i: `1.0` at `i=0`, `prod_{j=1` - (docs/v0.3.0-design.md §6.3 — "no re-derivation needed": the existing + ("no re-derivation needed": the existing stick-breaking encoding is already scale-free, so this is derivable from the batch's ground-truth stick logits alone, no `e_sec` required).""" cumprod = torch.cumprod(1.0 - fraction, dim=1) @@ -166,7 +165,7 @@ def _assemble_stage2_ar_inputs( ) -> dict[str, torch.Tensor]: """Ground-truth per-token AR conditioning tensors — all `(B, K_MAX, ...)` or `(B, K_MAX)`, built in one vectorized pass (teacher forcing means - every token's input is ground truth, docs/v0.3.0-design.md §6.2 point 3). + every token's input is ground truth). Keys match `Stage2Autoregressive.forward`'s trailing kwargs.""" device = sec_cont.device B, K = sec_cont.shape[0], sec_cont.shape[1] @@ -187,9 +186,10 @@ def _stage2_tf_prob( mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int ) -> float: """P(condition slot k+1 on the TRUE token k rather than the model's own - prediction), for the current epoch (docs/v0.3.0-design.md §3.3 - `stage2_model.autoregressive.teacher_forcing`). `"always"`/`"never"` are - the two degenerate constants; `"scheduled"` linearly interpolates + prediction), for the current epoch + (`stage2_model.autoregressive.teacher_forcing`). + `"always"`/`"never"` are the two degenerate constants; `"scheduled"` + linearly interpolates `p_start` (epoch 0) to `p_end` (the final epoch) — standard scheduled sampling (Bengio et al. 2015).""" if mode == "always": @@ -240,7 +240,7 @@ def _assemble_stage2_ar_inputs_scheduled( sample_steps: int, ) -> dict[str, torch.Tensor]: """Scheduled-sampling counterpart of `_assemble_stage2_ar_inputs` - (docs/v0.3.0-design.md §3.3 `teacher_forcing` = "scheduled"/"never"): + (`teacher_forcing` = "scheduled"/"never"): each slot's history is the TRUE previous token with probability `p_tf` (an independent per-example, per-slot Bernoulli draw) and the model's own free-running prediction otherwise — closing the train/inference gap that @@ -252,8 +252,8 @@ def _assemble_stage2_ar_inputs_scheduled( `giant.sample.sample_secondaries_ar` under `torch.no_grad()` — not a cheap one-step proxy, so building it costs the same `k_max` (`* steps` for flow) sequential forwards `sample.py` pays at inference, EVERY batch - this is called on (§6.4's cost note, paid at train time too whenever - teacher_forcing != "always"). Fully detached: gradient only ever flows + this is called on (paid at train time too whenever teacher_forcing != + "always"). Fully detached: gradient only ever flows through the "real" target path each stage trainer already uses (`_assemble_stage2_ar_target`), never through this self-sample. """ @@ -302,7 +302,7 @@ def _relax_onehot_type_slice( ) -> torch.Tensor: """Straight-through Gumbel-softmax relaxation of the per-slot type slice inside a flattened `(B, k_max * (cont_dim + type_dim))` WGAN generator - output — decision 5 (docs/v0.3.0-design.md §2.1): the forward pass is a + output: the forward pass is a hard one-hot (matching what the critic sees from real data), the backward pass flows smooth gradient. Continuous slots (stick/dir, and the type slice itself under `target = "embedding"`, which never calls @@ -311,8 +311,8 @@ def _relax_onehot_type_slice( `grad_probe`, if given, gets `["cont"]`/`["type"]` populated with the L2 norm of the gradient reaching this split point during the next `.backward()` call that touches it — a backward hook, not a second - backward pass. This is the §11.4 differentiability validation-obligation - instrumentation (docs/v0.3.0-design.md): the trunk-gradient contribution + backward pass. This is the differentiability validation-obligation + instrumentation: the trunk-gradient contribution from the type slice vs. the continuous slices, for `particle_type.target="onehot"` + `generator="wgan"`. Only ever populated on a `did_g_step` batch — the critic step backprops through diff --git a/giant/training/trainers.py b/giant/training/trainers.py index bc597dd..e1ac249 100644 --- a/giant/training/trainers.py +++ b/giant/training/trainers.py @@ -166,7 +166,7 @@ class StageSpec: tf_p_end=ar_cfg.get("tf_p_end", 1.0), # AR self-sampling under scheduled/never teacher forcing reuses # train.validate_steps as its flow-matching ODE step count — no - # dedicated config key for this (docs/v0.3.0-design.md §3.3 lists + # dedicated config key for this (the autoregressive config lists # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). ar_sample_steps=t["validate_steps"], ddpm_n_steps=stage_cfg.get("ddpm", {}).get("n_steps", 1000), @@ -184,12 +184,11 @@ class StageTrainer: Reads only the shared batch tuple `(cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx, sec_type_idx)` — stage 2 always conditions on the ground-truth `x1_s1` (`stage2_model.stage1_context = "truth"`, - stage-level teacher forcing; `"sampled"` is not implemented — see - docs/v0.3.0-design.md §3.3), so stage trainers never need each other's - output at train time. This means "stage-2-only training is a cheap - ablation, not new plumbing" (design doc §7) falls out for free: a trainer - only exists for active stages, and inactive stages are simply never - constructed. + stage-level teacher forcing; `"sampled"` is not implemented), so stage + trainers never need each other's output at train time. This means + "stage-2-only training is a cheap ablation, not new plumbing" falls out + for free: a trainer only exists for active stages, and inactive stages + are simply never constructed. Grad-norm clipping is per-stage here — v0.2's single shared optimizer clipped both stages' gradients jointly; splitting per stage is a small, @@ -381,7 +380,7 @@ class StageTrainer: non-adversarial and WGAN trainers. Gated on `n_sec_head is None`, not on `n_sec.mode`: a future - `mode="stop_token"` model (design doc §11.2, currently rejected in + `mode="stop_token"` model (currently rejected in `validate_config`) carries no head and would train its EOS signal in the generator/AR loss path instead, so this correctly stays zero. """ @@ -458,8 +457,7 @@ class FlowDDPMStageTrainer(StageTrainer): raise NotImplementedError( f"stage2_model.generator={spec.generator!r} is accepted by the " "schema but not implemented in v0.3.0 for stage 2 (only " - "'flow' and 'wgan' have a stage-2 secondary-decoder loss — " - "see docs/v0.3.0-design.md §11.2)" + "'flow' and 'wgan' have a stage-2 secondary-decoder loss)" ) super().__init__(spec, model, device) self.particle_type_lambda = self.particle_type_cfg.get("lambda", 1.0) @@ -562,7 +560,7 @@ class FlowDDPMStageTrainer(StageTrainer): ): """CE (`target="onehot"`) or MSE (`target="embedding"`) loss for the stage-2 model's `type_head` — the non-adversarial counterpart to - WGANStageTrainer's ST-Gumbel-into-the-critic path (decision 2/5). + WGANStageTrainer's ST-Gumbel-into-the-critic path. Zero when this stage has no `type_head` (stage 1, or `particle_type.target = "physical"`).""" l_type = torch.zeros((), device=device) @@ -774,8 +772,8 @@ class WGANStageTrainer(StageTrainer): "grad_norm_g", ] if self.is_stage2 and self.particle_type_cfg.get("target") == "onehot": - # §11.4 differentiability instrumentation — only meaningful when - # the type slice is a straight-through Gumbel relaxation. + # Differentiability instrumentation — only meaningful when the + # type slice is a straight-through Gumbel relaxation. train_keys += ["grad_norm_type_slice", "grad_norm_cont_slice"] self.train_metrics = [train_metric(key) for key in train_keys] self.val_metrics = [] @@ -860,11 +858,11 @@ class WGANStageTrainer(StageTrainer): ) if self.particle_type_cfg.get("target", "physical") == "onehot": # Straight-through Gumbel-softmax relaxation of the type - # slice only (decision 5) — the critic must see a hard - # one-hot forward (matching what "real" data looks like) - # while gradient still flows smoothly to the generator. - # grad_probe captures the §11.4 gradient-magnitude - # instrumentation — see _relax_onehot_type_slice's docstring. + # slice only — the critic must see a hard one-hot forward + # (matching what "real" data looks like) while gradient + # still flows smoothly to the generator. grad_probe captures + # the gradient-magnitude instrumentation — see + # _relax_onehot_type_slice's docstring. tau = _gumbel_tau( global_step, self.total_steps, @@ -898,7 +896,7 @@ class WGANStageTrainer(StageTrainer): ) # On a non-generator-step batch with no n_sec_head on this stage - # (n_sec now defaults to stage 2, decision 1), there's nothing for + # (n_sec now defaults to stage 2), there's nothing for # the generator optimizer to do this batch — g_loss would otherwise # be a graph-less zero tensor, which .backward() rejects outright. skip_g_step = not did_g_step and self.model.n_sec_head is None diff --git a/giant/validate.py b/giant/validate.py index ea8a34e..c193f4f 100644 --- a/giant/validate.py +++ b/giant/validate.py @@ -85,7 +85,7 @@ def validate_marginals( When `sec_decoder` is given, also validates Stage 2 via `giant.sample.sample_stage2`/`resolve_n_sec` (generator- and - one-shot-vs-autoregressive-agnostic, docs/v0.3.0-design.md §10): n_sec + one-shot-vs-autoregressive-agnostic): n_sec distribution (+ classification accuracy), per-slot energy-fraction marginals, and a particle-type marginal whose shape depends on `sec_decoder.particle_type_cfg["target"]` — restricted to each side's own diff --git a/scripts/check_migration_v02_v03.py b/scripts/check_migration_v02_v03.py index 20ff32e..3136d11 100644 --- a/scripts/check_migration_v02_v03.py +++ b/scripts/check_migration_v02_v03.py @@ -1,6 +1,5 @@ -"""Portal-machine follow-up for v0.3.0 step 2 (docs/v0.3.0-design.md §4.3): -diff a real v0.2 checkpoint's outputs against the new `build_models` on the -same input batch. +"""Portal-machine follow-up for v0.3.0 step 2: diff a real v0.2 checkpoint's +outputs against the new `build_models` on the same input batch. `tests/test_migration_v02_v03.py` already proves this bit-identical with synthetic random weights, but that test can't run where it matters (no @@ -16,7 +15,7 @@ Usage (from the repo root, on a portal machine): uv run python scripts/check_migration_v02_v03.py /ceph/lbogner/.../best.pt --batch 32 --seed 1 Run it once against a flow (or ddpm) checkpoint and once against a wgan -checkpoint (design doc §4.3's "one flow checkpoint and one WGAN checkpoint"). +checkpoint ("one flow checkpoint and one WGAN checkpoint"). A routed checkpoint (`model_config["router"]["enabled"]`) is only checked for successful construction — `giant.model.network.migrate_legacy_state_dict` doesn't yet remap routed (Expert-per-router) state dicts, so the @@ -115,8 +114,7 @@ def main() -> int: print( " routed checkpoint: migrate_legacy_state_dict only handles the " "monolithic trunk shape — verifying construction only, skipping " - "the bit-identical weight/output comparison. See " - "docs/v0.3.0-design.md §2.4's scope note." + "the bit-identical weight/output comparison." ) print("PASS (construction only, routed checkpoint)") return 0 diff --git a/scripts/dwarf.py b/scripts/dwarf.py index 311e384..5e7cff5 100644 --- a/scripts/dwarf.py +++ b/scripts/dwarf.py @@ -536,7 +536,7 @@ def warm_cache( "--material-conditioning", help="Must match the `giant train` run(s)' conditioning.material.type " "to warm for — independent of --particle-conditioning " - "(docs/v0.3.0-design.md §3.1: the two axes may differ)", + "(the two axes may differ)", ), ] = Conditioning.physical, router: Annotated[ diff --git a/scripts/warm_setup_cache.py b/scripts/warm_setup_cache.py index 115463c..3168b9e 100644 --- a/scripts/warm_setup_cache.py +++ b/scripts/warm_setup_cache.py @@ -31,8 +31,8 @@ def run_warm_setup_cache( select the normalizer cache entry (`giant.data.setup_cache.normalizer_key`) — pass the same values a later `giant train` invocation will use so it hits this warmed entry. The two - conditioning axes are independent (docs/v0.3.0-design.md §3.1) and may - differ. `router_enabled`/`router_type`/`n_experts` only matter for + conditioning axes are independent and may differ. + `router_enabled`/`router_type`/`n_experts` only matter for `router_type == "process"` (warms that `n_experts`'s process map); the energy-router quantile summary is always collected regardless, so a later `--router-type energy` run never needs to rescan just to seed diff --git a/tests/legacy/network_v02_snapshot.py b/tests/legacy/network_v02_snapshot.py index 8d34eb7..ea9364c 100644 --- a/tests/legacy/network_v02_snapshot.py +++ b/tests/legacy/network_v02_snapshot.py @@ -1,13 +1,13 @@ """Frozen snapshot of `giant/model/network.py` as it stood at the v0.3.0 -"step 1" commit (eb6dd27), i.e. the last commit before the step-2 §5 -decomposition (see `docs/v0.3.0-design.md`). +"step 1" commit (eb6dd27), i.e. the last commit before the step-2 +composable-parts decomposition. This is a deliberate verbatim copy, not an import of the live module — the whole point is that this file's classes keep behaving exactly as v0.2 did even after `giant/model/network.py` itself is rewritten, so `tests/test_migration_v02_v03.py` has a stable "old" side to diff the new -`build_models`/`Stage1Model`/`Stage2OneShot` against (design doc §4.3's -bit-identical acceptance test). Do not edit this file to track future +`build_models`/`Stage1Model`/`Stage2OneShot` against (the bit-identical +acceptance test). Do not edit this file to track future `network.py` changes — it exists specifically to stop tracking them. """ diff --git a/tests/test_cli_train_overrides.py b/tests/test_cli_train_overrides.py index 2341b69..486ea4b 100644 --- a/tests/test_cli_train_overrides.py +++ b/tests/test_cli_train_overrides.py @@ -1,6 +1,5 @@ -"""Tests for `giant train`'s stage-prefixed CLI flags (docs/v0.3.0-design.md -decision 7 / docs/v0.3.0-followups.md item 2): --stage1-*/--stage2-* must -independently override each stage's config block, and must take precedence +"""Tests for `giant train`'s stage-prefixed CLI flags: --stage1-*/--stage2-* +must independently override each stage's config block, and must take precedence over the older shared flags (--mode/--hidden-dim/--n-critic/... ) that still apply the same value to both stages for backward compatibility.""" diff --git a/tests/test_config.py b/tests/test_config.py index ca50de6..fb7b5c2 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -585,8 +585,8 @@ def test_validate_config_embedding_target_passes_with_embedding_conditioning(): def test_validate_config_mixed_particle_material_conditioning_is_valid(): - """docs/v0.3.0-design.md §3.1: the particle and material conditioning - axes are configured independently and may mix freely — e.g. material + """The particle and material conditioning axes are configured + independently and may mix freely — e.g. material "physical" with particle "embedding" — and the data pipeline (giant/data/transforms.py) now implements that end-to-end, so validate_config must not reject it.""" @@ -638,8 +638,8 @@ def test_validate_config_stop_token_not_implemented(): def test_validate_config_n_sec_truth_rejected_for_rollout_capable_checkpoint(): - """docs/v0.3.0-design.md §9: 'n_sec.mode = "truth" is invalid for a - rollout-capable checkpoint' — both stages active means giant rollout + """'n_sec.mode = "truth" is invalid for a rollout-capable checkpoint' — + both stages active means giant rollout could load this checkpoint, but 'truth' has no ground truth to draw n_sec from at rollout time.""" cfg = _cfg_with( diff --git a/tests/test_loader.py b/tests/test_loader.py index 23003a8..ad31f7a 100644 --- a/tests/test_loader.py +++ b/tests/test_loader.py @@ -210,8 +210,7 @@ def test_build_topn_map_from_files_fewer_values_than_n_classes(tmp_path): def test_build_pdg_topn_map_from_files_pools_primary_and_secondary_pdg(tmp_path): """A species that's rare as a primary but common as a secondary must still rank by its pooled (primary + secondary) count, not just its - primary-role count alone — the whole point of pooling both roles - (docs/v0.3.0-design.md §8).""" + primary-role count alone — the whole point of pooling both roles.""" path = tmp_path / "a.parquet" # primary pdg: mostly 11 (electron), one lone 22 (photon) pdg = [11] * 5 + [22] * 1 diff --git a/tests/test_migration_v02_v03.py b/tests/test_migration_v02_v03.py index 6c5d10c..05829a3 100644 --- a/tests/test_migration_v02_v03.py +++ b/tests/test_migration_v02_v03.py @@ -1,13 +1,12 @@ -"""Migration acceptance test for v0.3.0 step 2 (docs/v0.3.0-design.md §4.3, -§12 step 2): "load a v0.2 checkpoint through migrate_config + the new -build_models, and diff its outputs against v0.2 code on the same input -batch — bit-identical, or the refactor has changed something it should not -have." +"""Migration acceptance test for v0.3.0 step 2: "load a v0.2 checkpoint +through migrate_config + the new build_models, and diff its outputs against +v0.2 code on the same input batch — bit-identical, or the refactor has +changed something it should not have." No `/ceph` access on this machine (see CLAUDE.md's Compute environment -section), so a real trained checkpoint can't be used here — see -docs/v0.3.0-design.md's plan for the separate portal-machine follow-up with a -real checkpoint. This test is the synthetic stand-in: build a v0.2-shaped +section), so a real trained checkpoint can't be used here — a separate +portal-machine follow-up with a real checkpoint is planned instead. This +test is the synthetic stand-in: build a v0.2-shaped model from the frozen `tests/legacy/network_v02_snapshot.py` classes with fixed-seed random weights (playing the role of "a v0.2 checkpoint"), migrate its config and remap its state dict onto the new `build_models` output, and @@ -134,7 +133,7 @@ def _run_migration_check(mode: str, conditioning: str) -> None: assert isinstance(new_stage1, net.Stage1Model) assert isinstance(new_stage2, net.Stage2OneShot) # legacy_owner="stage1": n_sec lives on stage1, not stage2, for a - # migrated v0.2 checkpoint (design doc §4.1). + # migrated v0.2 checkpoint. assert new_stage1.n_sec_head is not None assert new_stage2.n_sec_head is None @@ -199,12 +198,11 @@ def test_migrate_legacy_model_config_shape(): def test_migrate_legacy_model_config_nonzero_expert_dims_raises(): - """docs/v0.3.0-followups.md item 8 regression: a v0.2 checkpoint's - model_config carrying a non-default expert_hidden_dim/expert_n_blocks - must fail loudly through this path too (§4.2) — not just - giant.config.migrate_config's parallel TOML-load path. Silently dropping - these keys (build_router's kwarg filtering) would resize the experts - instead of refusing.""" + """Regression: a v0.2 checkpoint's model_config carrying a non-default + expert_hidden_dim/expert_n_blocks must fail loudly through this path too + — not just giant.config.migrate_config's parallel TOML-load path. + Silently dropping these keys (build_router's kwarg filtering) would + resize the experts instead of refusing.""" legacy_cfg = _legacy_model_config(mode="flow", conditioning="physical") legacy_cfg["router"] = { "enabled": True, diff --git a/tests/test_network.py b/tests/test_network.py index 0a254f6..bbf0552 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -84,7 +84,7 @@ def test_stage1_model_gradients_flow(): def test_stage1_model_no_n_sec_head_by_default(): """Fresh v0.3.0 construction (no n_sec_head_k_max) has no n_sec head — - decision 1 (docs/v0.3.0-design.md §2) moves it to stage 2.""" + it moves to stage 2.""" model = Stage1Model( pdg_vocab=3, mat_vocab=2, particle_cfg=PARTICLE_CFG, material_cfg=MATERIAL_CFG ) @@ -206,7 +206,7 @@ def test_condition_encoder_onehot_is_a_true_one_hot_vector(): assert torch.all(pdg_e.sum(dim=-1) == 1.0) -# --- Stage2OneShot particle_type architecture (docs/v0.3.0-design.md decision 2) -- +# --- Stage2OneShot particle_type architecture -------------------------------- def _build_stage2(target: str, generator: str, emb_dim: int = 6) -> Stage2OneShot: @@ -300,7 +300,7 @@ def test_stage2_oneshot_forward_shape_onehot_flow_excludes_type(): assert out.shape == (B, k_max * CONT_SLOT_DIM) -# --- MarkovHistory (docs/v0.3.0-design.md §6.2) ----------------------------- +# --- MarkovHistory ----------------------------------------------------------- def test_markov_history_shape(): @@ -314,8 +314,8 @@ def test_markov_history_shape(): def test_markov_history_uses_start_vector_when_no_prev(): """Slot 0's own raw feature must be ignored — a learned start vector is - substituted there instead (a reasonable default not specified by the - design doc, see Stage2Autoregressive's docstring).""" + substituted there instead (a reasonable default, see + Stage2Autoregressive's docstring).""" hist = MarkovHistory(in_dim=4, out_dim=6) B, K = 2, 3 has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) @@ -328,7 +328,7 @@ def test_markov_history_uses_start_vector_when_no_prev(): assert torch.allclose(out_a[:, 1:], out_b[:, 1:]) -# --- AttentionHistory (docs/v0.3.0-design.md §6.2, v0.3.0 step 7) ---------- +# --- AttentionHistory (v0.3.0 step 7) --------------------------------------- def test_attention_history_shape(): @@ -392,7 +392,7 @@ def test_attention_history_step_matches_forward(): assert torch.allclose(stepped, expected, atol=1e-5) -# --- Stage2Autoregressive (docs/v0.3.0-design.md §6, v0.3.0 step 5) --------- +# --- Stage2Autoregressive (v0.3.0 step 5) ----------------------------------- def _build_stage2_ar( @@ -671,8 +671,8 @@ def test_build_models_share_stages_true_shared_params_are_in_both_stage_paramete """The shared encoder's parameters must actually appear in both stages' own `.parameters()` — that's what makes each stage's independent optimizer include (and update) them, which is the actual mechanism behind - "shared weights, forced common representation" (docs/v0.3.0-design.md - §3.1), not just object identity on `.cond_enc`.""" + "shared weights, forced common representation", not just object identity + on `.cond_enc`.""" built = build_models(_minimal_model_config(share_stages=True)) stage1, stage2 = built["stage1"], built["stage2"] assert stage1 is not None and stage2 is not None diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index f202b9d..fae62c5 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -155,7 +155,7 @@ def test_run_train_job_second_run_hits_cache(tmp_path, data, monkeypatch): def test_run_train_job_builds_caches_and_persists_pdg_topn_map(tmp_path, data): """DEFAULT_CONFIG's stage2_model.particle_type.target defaults to - "onehot" (docs/v0.3.0-design.md §3.3/§8) — a plain _tiny_cfg() run must + "onehot" — a plain _tiny_cfg() run must build the shared pdg top-N map, cache it in the setup-cache sidecar, and persist it into the checkpoint, with no extra config needed.""" echo1 = _run(data, tmp_path / "out1") @@ -242,7 +242,7 @@ def test_run_train_job_new_val_fraction_is_partial_hit(tmp_path, data, monkeypat def test_run_train_job_custom_k_max_end_to_end(tmp_path, data): - """docs/v0.3.0-followups.md item 3 regression: stage2_model.k_max other + """Regression: stage2_model.k_max other than the K_MAX module constant's default (15) must not produce a shape mismatch between the data pipeline (loader.py/transforms.py padding) and the model (network.py's trunks, sized from this same config value).""" @@ -254,9 +254,9 @@ def test_run_train_job_custom_k_max_end_to_end(tmp_path, data): def test_run_train_job_mixed_particle_material_conditioning_end_to_end(tmp_path, data): - """docs/v0.3.0-followups.md item 4 regression: conditioning.particle.type + """Regression: conditioning.particle.type and conditioning.material.type are configured independently and may mix - freely (docs/v0.3.0-design.md §3.1) — e.g. particle "embedding" with + freely — e.g. particle "embedding" with material "physical" — end-to-end through the real data pipeline, not just accepted by validate_config.""" cfg = _tiny_cfg() @@ -286,7 +286,7 @@ def test_run_train_job_mixed_particle_material_conditioning_end_to_end(tmp_path, def test_run_train_job_share_stages_end_to_end(tmp_path, data): - """docs/v0.3.0-followups.md item 5 regression: conditioning.share_stages + """Regression: conditioning.share_stages = true must actually train (not raise NotImplementedError), and the resulting checkpoint's two stages must reload into a single shared ConditionEncoder instance rather than two independent ones.""" diff --git a/tests/test_rollout.py b/tests/test_rollout.py index 7c230b2..da8d624 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -373,7 +373,7 @@ def _models_v3( particle_cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1} material_cfg = {"type": conditioning, "emb_dim": emb_dim, "n_layers": 1} # A fresh v0.3.0 Stage1Model — no n_sec_head_k_max, unlike _models() above - # (decision 1 moves n_sec ownership to stage 2 by default). + # (n_sec ownership moves to stage 2 by default). s1 = Stage1Model( pdg_vocab=3, mat_vocab=2, @@ -470,9 +470,9 @@ def _run_v3( def test_rollout_stage2_owns_n_sec_when_stage1_has_no_head(fake_material_props): - """A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1) — n_sec - must come from Stage2's own head instead, and the run must still - complete and conserve energy.""" + """A fresh v0.3.0 Stage1Model has no n_sec_head — n_sec must come from + Stage2's own head instead, and the run must still complete and + conserve energy.""" s1, s2 = _models_v3() rec = _run_v3(s1, s2) assert len(rec["event_id"]) > 0 @@ -501,7 +501,7 @@ def test_rollout_physical_target_decoder_generator_matrix( ): """Every (decoder, stage2 generator) combination under particle_type.target="physical" must run to completion and conserve - energy — the matrix docs/v0.3.0-design.md §7 calls out for comparison.""" + energy.""" s1, s2 = _models_v3(decoder=decoder, generator2=generator2) rec = _run_v3(s1, s2) assert len(rec["event_id"]) > 0 @@ -555,10 +555,10 @@ def test_rollout_onehot_target_missing_topn_map_raises(fake_material_props): _run_v3(s1, s2, pdg_topn_map=None) -# --- conditioning.{particle,material}.type = "onehot" (docs/v0.3.0-followups.md -# item 7) — a separate axis from stage2_model.particle_type.target above: this -# is what feeds cond_cat's extra top-N columns for ConditionEncoder's own -# "onehot" mode, not the secondary-species decode. --------------------------- +# --- conditioning.{particle,material}.type = "onehot" — a separate axis from +# stage2_model.particle_type.target above: this is what feeds cond_cat's +# extra top-N columns for ConditionEncoder's own "onehot" mode, not the +# secondary-species decode. --------------------------------------------- COND_PDG_TOPN_MAP = TopNMap(class_map=dict(PDG_MAP), other_members={}) COND_MAT_TOPN_MAP = TopNMap(class_map={"G4_AIR": 0, "G4_PbWO4": 1}, other_members={}) @@ -653,7 +653,7 @@ def test_rollout_embedding_target_end_to_end(decoder): def test_l1_dist_collector_populated_only_for_embedding_target(): - """§11.3: the L1-distance diagnostic only makes sense under + """The L1-distance diagnostic only makes sense under particle_type.target="embedding" — a physical-target run must leave the collector empty rather than silently accumulating garbage.""" s1, s2 = _models_v3(target="physical") diff --git a/tests/test_router.py b/tests/test_router.py index 8d791d7..01c0ded 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -896,8 +896,8 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow(): cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) assert stage1_norm.shape == (B, X_DIM) - # A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1 moves it to - # stage 2) — sample_flow returns n_sec_pred=None here, and n_sec must be + # A fresh v0.3.0 Stage1Model has no n_sec_head (it moves to stage 2) — + # sample_flow returns n_sec_pred=None here, and n_sec must be # asked of stage2 instead, using the just-sampled stage1_norm as context. assert n_sec_pred is None n_sec_pred = stage2.predict_n_sec(cond_cont, cond_cat, stage1_norm).argmax(dim=-1) @@ -1092,8 +1092,8 @@ def test_build_models_routed_pair_is_drop_in_for_sample_flow(): cond_cont, cond_cat = _cond(B, pdg=3, mat=2) stage1_norm, n_sec_pred = sample_flow(stage1, cond_cont, cond_cat, steps=2) assert stage1_norm.shape == (B, X_DIM) - # A fresh v0.3.0 Stage1Model has no n_sec_head (decision 1 moves it to - # stage 2) — sample_flow returns n_sec_pred=None here, and n_sec must be + # A fresh v0.3.0 Stage1Model has no n_sec_head (it moves to stage 2) — + # sample_flow returns n_sec_pred=None here, and n_sec must be # asked of stage2 instead, using the just-sampled stage1_norm as context. assert n_sec_pred is None n_sec_pred = stage2.predict_n_sec(cond_cont, cond_cat, stage1_norm).argmax(dim=-1) diff --git a/tests/test_sample.py b/tests/test_sample.py index ff52610..f6b12e7 100644 --- a/tests/test_sample.py +++ b/tests/test_sample.py @@ -1,6 +1,6 @@ """Tests for giant/sample.py's v0.3.0 stage-model sampling — the AR loop (`sample_secondaries_ar`) and non-"physical" `particle_type.target` coverage -for the one-shot samplers (docs/v0.3.0-design.md step 6).""" +for the one-shot samplers.""" import pytest import torch @@ -38,7 +38,7 @@ def _cond(B: int, pdg: int = 3, mat: int = 2) -> tuple[torch.Tensor, torch.Tenso def _conditioning_for(target: str) -> str: # target="embedding" regresses against the conditioning's own embedding - # table (docs/v0.3.0-design.md §3.3) — only meaningful when the + # table — only meaningful when the # conditioning axis is itself "embedding". return "embedding" if target == "embedding" else "physical" @@ -103,7 +103,7 @@ def _expected_type_dim(target: str, emb_dim: int) -> int: return PARTICLE_PHYS_DIM if target == "physical" else emb_dim -# ── Stage-1 n_sec ownership (decision 1) ──────────────────────────────────── +# ── Stage-1 n_sec ownership ────────────────────────────────────────────────── def test_sample_flow_returns_none_n_sec_when_stage1_owns_no_head(): diff --git a/tests/test_train.py b/tests/test_train.py index 992adb8..ad14113 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -90,7 +90,7 @@ def test_wandb_run_config_handles_missing_model_config(): assert wcfg["model_config"] == {} -# --- AR helper functions (v0.3.0 step 5, docs/v0.3.0-design.md §6) --------- +# --- AR helper functions (v0.3.0 step 5) --------- def test_stick_fraction_matches_sigmoid_of_logit(): @@ -119,7 +119,7 @@ def test_ar_has_prev_false_only_at_slot_zero(): assert has_prev.tolist() == [[False, True, True, True, True]] -# --- _stage2_tf_prob (docs/v0.3.0-design.md §3.3, v0.3.0 step 7) ----------- +# --- _stage2_tf_prob (v0.3.0 step 7) ----------- def test_stage2_tf_prob_always_is_constant_one(): @@ -569,7 +569,7 @@ def test_metrics_csv_columns_are_stage_prefixed(): def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): """Regression test: on a non-generator-step batch, if this stage's model - has no n_sec_head (n_sec defaults to stage 2, decision 1), g_loss is a + has no n_sec_head (n_sec defaults to stage 2), g_loss is a graph-less zero — .backward() must not be called on it.""" cfg = _base_cfg() cfg["stage1_model"]["generator"] = "wgan" @@ -672,11 +672,11 @@ def test_train_end_to_end_ar_attention_history_scheduled_teacher_forcing( def test_ar_wgan_onehot_grad_norm_instrumentation_populates_metrics(): - """§11.4 differentiability validation-obligation instrumentation: the + """Differentiability validation-obligation instrumentation: the trunk-gradient-norm-by-slice columns must appear and actually fire for generator='wgan' + particle_type.target='onehot' under decoder= - 'autoregressive' (added at v0.3.0 step 5 per the design doc's - instruction to accrue evidence during the architecture comparison).""" + 'autoregressive' (added at v0.3.0 step 5 to accrue evidence during the + architecture comparison).""" cfg = _base_cfg() cfg["stage2_model"]["decoder"] = "autoregressive" cfg["stage2_model"]["particle_type"] = {"target": "onehot", "lambda": 1.0} diff --git a/tests/test_type_embedding_distance.py b/tests/test_type_embedding_distance.py index c17f965..6b3b8dd 100644 --- a/tests/test_type_embedding_distance.py +++ b/tests/test_type_embedding_distance.py @@ -1,5 +1,5 @@ """Tests for the secondary-type embedding-distance diagnostic -(giant.analysis.type_embedding_distance) — the §11.3 diagnostic.""" +(giant.analysis.type_embedding_distance).""" from __future__ import annotations diff --git a/tests/test_validate.py b/tests/test_validate.py index 533c933..1e29517 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -11,8 +11,8 @@ _K_MAX = 5 def _tiny_models(particle_type_cfg: dict | None = None): - """A fresh v0.3.0 pair: Stage1Model owns no n_sec_head (decision 1), so - n_sec always comes from Stage2OneShot.""" + """A fresh v0.3.0 pair: Stage1Model owns no n_sec_head, so n_sec always + comes from Stage2OneShot.""" s1 = Stage1Model( pdg_vocab=3, mat_vocab=2, diff --git a/uv.lock b/uv.lock index bd8cb35..fb16763 100644 --- a/uv.lock +++ b/uv.lock @@ -534,7 +534,7 @@ wheels = [ [[package]] name = "giant" -version = "0.2.0" +version = "0.3.0" source = { editable = "." } dependencies = [ { name = "numpy" }, -- 2.39.5 From adb7a8663e9edf34ecac5a43dc0f38d10d4363a0 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:25:50 +0200 Subject: [PATCH 18/30] Add pytest-cov to dev deps and run coverage in CI Test job now reports coverage (term + xml) and uploads it as a build artifact, so coverage regressions are visible per-PR. Co-Authored-By: Claude Sonnet 5 --- .gitea/workflows/ci.yml | 6 ++- .gitignore | 5 ++ pyproject.toml | 11 ++++ uv.lock | 115 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 136 insertions(+), 1 deletion(-) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index e69fc65..16e3562 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -82,7 +82,11 @@ jobs: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - - run: uv run pytest + - run: uv run pytest --cov --cov-report=term-missing --cov-report=xml + - uses: actions/upload-artifact@v4 + with: + name: coverage-report + path: coverage.xml sync-version-on-tag: name: Sync project version with tag diff --git a/.gitignore b/.gitignore index 95aeaf8..fdad9a8 100644 --- a/.gitignore +++ b/.gitignore @@ -20,3 +20,8 @@ checkpoints/ # giant analyze run directories (shared.json, reduced/, plots/, condor logs) /analysis_runs/ + +# Coverage artifacts +.coverage +coverage.xml +htmlcov/ diff --git a/pyproject.toml b/pyproject.toml index 0e0288e..a8c08f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,7 @@ cuda = [ ] dev = [ "pytest>=8,<10", + "pytest-cov>=5,<8", "ruff>=0.15,<1", "ty>=0.0.50,<0.1", "giant[convert,analysis,geometry,wandb]", @@ -51,6 +52,16 @@ analysis = [ giant = "giant.cli:app" dwarf = "scripts.dwarf:app" +[tool.coverage.run] +source = ["giant", "scripts"] +omit = ["*/legacy/*"] + +[tool.coverage.report] +exclude_also = [ + "if TYPE_CHECKING:", + "raise NotImplementedError", +] + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/uv.lock b/uv.lock index fb16763..97f81e7 100644 --- a/uv.lock +++ b/uv.lock @@ -357,6 +357,105 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = 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stage1_hidden_dim: Annotated[ -- 2.39.5 From 24445b7427fe9b5d0becced3175d0bc32fefa513 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:38:04 +0200 Subject: [PATCH 20/30] Add coverage for router-center seeding, geometry batch reader, material topN cache, and setup-cache corruption paths Closes the highest-value coverage gaps found via pytest-cov: pipeline.py's EnergyRouter quantile-seeding (the roadmap's flagged fix for the failed MoE rollout benchmark) had zero coverage, geometry.py's real parquet-batch reader was always mocked, the material top-N-map cache-hit branch was untested (only pdg's was), and setup_cache.py was missing malformed-cache-body and unknown-axis error paths. Co-Authored-By: Claude Sonnet 5 --- tests/test_geometry.py | 71 ++++++++++++++++++++++++++++++++ tests/test_pipeline.py | 86 ++++++++++++++++++++++++++++++++++++++- tests/test_setup_cache.py | 32 +++++++++++++++ 3 files changed, 188 insertions(+), 1 deletion(-) diff --git a/tests/test_geometry.py b/tests/test_geometry.py index d967315..9e9aace 100644 --- a/tests/test_geometry.py +++ b/tests/test_geometry.py @@ -4,6 +4,7 @@ from pathlib import Path from unittest.mock import patch import numpy as np +import pandas as pd import pytest from giant import geometry as g @@ -12,6 +13,76 @@ from giant import geometry as g pytest.importorskip("sklearn") +def _steps_frame(n=5, with_post=True): + rng = np.random.default_rng(0) + data = { + "pre_x": rng.uniform(-10, 10, n), + "pre_y": rng.uniform(-10, 10, n), + "pre_z": rng.uniform(-10, 10, n), + "material": ["G4_AIR"] * n, + "layer_id": np.arange(n, dtype=np.int64), + } + if with_post: + data["post_x"] = rng.uniform(-10, 10, n) + data["post_y"] = rng.uniform(-10, 10, n) + data["post_z"] = rng.uniform(-10, 10, n) + return pd.DataFrame(data) + + +def test_iter_point_batches_missing_columns_raises(tmp_path): + path = tmp_path / "steps.parquet" + pd.DataFrame({"pre_x": [0.0]}).to_parquet(path) + with pytest.raises(ValueError, match="missing columns"): + next(g._iter_point_batches(path)) + + +def test_iter_point_batches_without_post_columns_yields_pre_only(tmp_path): + path = tmp_path / "steps.parquet" + df = _steps_frame(n=5, with_post=False) + df.to_parquet(path) + + (pos, mat, lay) = next(g._iter_point_batches(path)) + + assert pos.shape == (5, 3) + np.testing.assert_allclose( + pos, df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32) + ) + assert list(mat) == ["G4_AIR"] * 5 + np.testing.assert_array_equal(lay, np.arange(5)) + + +def test_iter_point_batches_with_post_columns_doubles_and_concatenates_points( + tmp_path, +): + path = tmp_path / "steps.parquet" + df = _steps_frame(n=5, with_post=True) + df.to_parquet(path) + + (pos, mat, lay) = next(g._iter_point_batches(path)) + + # Every step contributes both its pre_pos and post_pos, sharing the + # step's material/layer_id label — so batches double in length. + assert pos.shape == (10, 3) + np.testing.assert_allclose( + pos[:5], df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32) + ) + np.testing.assert_allclose( + pos[5:], df[["post_x", "post_y", "post_z"]].to_numpy(dtype=np.float32) + ) + assert list(mat) == ["G4_AIR"] * 10 + np.testing.assert_array_equal(lay, np.concatenate([np.arange(5), np.arange(5)])) + + +def test_iter_point_batches_respects_batch_size(tmp_path): + path = tmp_path / "steps.parquet" + df = _steps_frame(n=10, with_post=False) + df.to_parquet(path, row_group_size=10) + + batches = list(g._iter_point_batches(path, batch_size=4)) + + assert [len(pos) for pos, _, _ in batches] == [4, 4, 2] + + def _box_batch(n, rng): """A labelled point cloud: inside a 100mm box -> PbWO4/0, else AIR/-1.""" pos = rng.uniform(-200, 200, (n, 3)).astype(np.float32) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index fae62c5..c0848c9 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -6,9 +6,10 @@ import pytest import torch from giant import config as gconfig +from giant.constants import COND_DIM from giant.data import setup_cache from giant.data.transforms import Normalizer -from giant.pipeline import run_train_job +from giant.pipeline import _seed_energy_router, run_train_job def _unit(v): @@ -175,6 +176,29 @@ def test_run_train_job_builds_caches_and_persists_pdg_topn_map(tmp_path, data): assert any("pdg top-N map: cache hit" in m for m in echo2) +def test_run_train_job_builds_caches_and_persists_material_topn_map(tmp_path, data): + """conditioning.material.type="onehot" is an independent axis from the + pdg one above, with its own build/cache-hit branch in run_setup_stage — + exercise both here the same way the pdg test above does.""" + cfg = _tiny_cfg() + cfg["conditioning"]["material"]["type"] = "onehot" + echo1 = _run(data, tmp_path / "out1", cfg=cfg) + assert any("building material top-N map" in m for m in echo1) + + loaded = setup_cache.load(data, [data]) + assert loaded is not None + key = setup_cache.topn_key("material", 4) # conditioning.material.emb_dim = 4 + assert key in loaded.topn_maps + assert set(loaded.topn_maps[key].class_map.keys()) >= {"G4_AIR", "G4_Fe"} + + ckpt = torch.load(tmp_path / "out1" / "last.pt", weights_only=False) + assert "mat_topn_map" in ckpt + assert set(ckpt["mat_topn_map"]["class_map"].keys()) >= {"G4_AIR", "G4_Fe"} + + echo2 = _run(data, tmp_path / "out2", cfg=cfg) + assert any("material top-N map: cache hit" in m for m in echo2) + + def test_run_train_job_no_topn_map_for_physical_target(tmp_path, data): cfg = _tiny_cfg() cfg["stage2_model"]["particle_type"] = {"target": "physical", "lambda": 1.0} @@ -326,3 +350,63 @@ def test_run_train_job_matches_uncached_output(tmp_path, data): ) assert uncached["pdg_map"] == cached["pdg_map"] assert uncached["mat_map"] == cached["mat_map"] + + +def _fitted_cond_norm(seed=0): + rng = np.random.default_rng(seed) + return Normalizer().fit(rng.normal(size=(64, COND_DIM)).astype(np.float32)) + + +@pytest.mark.parametrize( + "router_cfg", + [ + {"enabled": False, "type": "energy", "n_experts": 4}, + {"enabled": True, "type": "pdg", "n_experts": 4}, + ], +) +def test_seed_energy_router_noop_when_not_an_enabled_energy_router(router_cfg): + cond_norm = _fitted_cond_norm() + echoed = [] + _seed_energy_router(router_cfg, cond_norm, np.array([1.0, 2.0]), 3, echoed.append) + assert "centers_init" not in router_cfg + assert echoed == [] + + +def test_seed_energy_router_falls_back_to_default_and_warns_when_no_samples(): + router_cfg = {"enabled": True, "type": "energy", "n_experts": 4} + cond_norm = _fitted_cond_norm() + echoed = [] + _seed_energy_router( + router_cfg, cond_norm, np.empty(0), energy_idx=3, echo=echoed.append + ) + assert "centers_init" not in router_cfg + assert len(echoed) == 1 + assert "falls back to default centers" in echoed[0] + + +def test_seed_energy_router_seeds_centers_from_data_quantiles(): + router_cfg = {"enabled": True, "type": "energy", "n_experts": 4} + cond_norm = _fitted_cond_norm() + energy_idx = 3 + # A grid of "raw" quantile values as setup_cache.energy_quantiles_from_sample + # would produce them: monotonically increasing, in the same (log-energy) + # units as the conditioning column being normalized against. + energy_quantiles = np.linspace(1.0, 10.0, 33).astype(np.float32) + echoed = [] + _seed_energy_router( + router_cfg, cond_norm, energy_quantiles, energy_idx, echoed.append + ) + + assert "centers_init" in router_cfg + centers = np.asarray(router_cfg["centers_init"], dtype=np.float32) + assert centers.shape == (router_cfg["n_experts"],) + + levels = np.linspace(0.0, 1.0, router_cfg["n_experts"]) + raw_centers = setup_cache.energy_quantile_at(energy_quantiles, levels) + expected = (raw_centers - cond_norm.mean[energy_idx]) / cond_norm.std[energy_idx] + np.testing.assert_allclose(centers, expected, rtol=1e-5) + # Quantile levels are increasing, and the normalizer's std is positive, so + # the seeded centers must preserve that order rather than e.g. reversing it. + assert np.all(np.diff(centers) > 0) + assert len(echoed) == 1 + assert "seeded EnergyRouter centers" in echoed[0] diff --git a/tests/test_setup_cache.py b/tests/test_setup_cache.py index 9224b64..70e8e40 100644 --- a/tests/test_setup_cache.py +++ b/tests/test_setup_cache.py @@ -128,6 +128,11 @@ def test_save_load_round_trip_topn_maps(tmp_path): assert mat_m.class_map == {"G4_AIR": 0, "PbWO4": 1} +def test_topn_key_unknown_axis_raises(): + with pytest.raises(ValueError, match="unknown top-N map axis"): + setup_cache.topn_key("process", 4) + + def test_load_missing_sidecar_returns_none(tmp_path): data = _touch_parquet(tmp_path / "shard.parquet") assert setup_cache.load(data, [data]) is None @@ -176,6 +181,25 @@ def test_load_invalidates_on_file_content_change(tmp_path): assert setup_cache.load(data, files) is None +def test_load_returns_none_on_malformed_cache_body(tmp_path): + """format_version/dims/fingerprint all check out, but the cache body + itself doesn't match SetupCache.from_json's expected shape (e.g. hand- + edited or written by a version that changed a nested key) — a clean + miss, not a crash.""" + data = _touch_parquet(tmp_path / "shard.parquet") + files = [data] + setup_cache.save(data, files, SetupCache.empty(files)) + + path = setup_cache.sidecar_path(data) + raw = json.loads(path.read_text()) + raw["vocab"] = {"pdg_map": {"11": 0}} # missing required "mat_map" key + path.write_text(json.dumps(raw)) + + echoed = [] + assert setup_cache.load(data, files, echo=echoed.append) is None + assert any("malformed" in m for m in echoed) + + def test_load_soft_warns_on_git_hash_mismatch_but_still_hits(tmp_path, capsys): data = _touch_parquet(tmp_path / "shard.parquet") files = [data] @@ -352,3 +376,11 @@ def test_compute_event_index_from_files_single_file_unaffected(tmp_path): np.testing.assert_array_equal(unique_ids, [5, 7]) np.testing.assert_array_equal(counts, [2, 1]) + + +def test_compute_event_index_from_files_empty_file_list(): + unique_ids, counts = setup_cache.compute_event_index_from_files([]) + assert unique_ids.size == 0 + assert counts.size == 0 + assert unique_ids.dtype == np.int64 + assert counts.dtype == np.int64 -- 2.39.5 From b1cf9d345dcfe491dc569a575eded8a8a561002d Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:38:56 +0200 Subject: [PATCH 21/30] Downgrade coverage-report upload to actions/upload-artifact@v3 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit v4 requires the @actions/artifact v2 backend, which this self-hosted Gitea instance doesn't support yet (GHESNotSupportedError) — v3 uses the older API Gitea's Actions runner implements. Co-Authored-By: Claude Sonnet 5 --- .gitea/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index 16e3562..478bbb9 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -83,7 +83,7 @@ jobs: echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run pytest --cov --cov-report=term-missing --cov-report=xml - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v3 with: name: coverage-report path: coverage.xml -- 2.39.5 From 82772e4e099a01c0d9ccc6bec1c504cae5b626fe Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:47:31 +0200 Subject: [PATCH 22/30] Add render.py coverage: figure params, router diagnostics plots, gallery/condor glue MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit render.py was at 58% coverage — the module's plotting dispatch (router_gating, router_share, unavailable) and glue logic (_figure_params/_figure_params_v2, _plot_metadata, render_all's gallery subprocess call, render_run's condor RunMeta wiring) had no tests at all. Brings it to 100%: pure-function unit tests for the v0.2/v0.3.0 figure-param branches and _plot_metadata, real LaTeX-rendered fixtures for the previously-untested plot kinds and a 4-group grouped_hist (exercises the hidden-leftover-axis branch), and mocked subprocess/condor calls to isolate render_all/render_run's own logic. Co-Authored-By: Claude Sonnet 5 --- tests/test_render.py | 276 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 276 insertions(+) diff --git a/tests/test_render.py b/tests/test_render.py index 49bb84c..f5691c1 100644 --- a/tests/test_render.py +++ b/tests/test_render.py @@ -4,10 +4,12 @@ from __future__ import annotations from pathlib import Path +import numpy as np import pytest pytest.importorskip("plotstyle") +from giant.analysis import render as render_mod # noqa: E402 from giant.analysis.reduced import Reduced # noqa: E402 @@ -19,6 +21,148 @@ def _try_render(reduced: list[Reduced], out: Path): return render_all(out / "reduced", out / "plots") +def test_render_router_diagnostics_and_edge_cases(tmp_path: Path): + reduced = [ + Reduced( + "rg", + "router", + "router_gating", + "Router gating", + "pre-step energy [MeV]", + { + "n_experts": 2, + "log_x": True, + "router_type": "energy", + "rollout": { + "centers": [1.0, 10.0, 100.0], + "means": [[0.6, 0.4], [0.5, 0.5], [0.4, 0.6]], + }, + "reference": { + "centers": [1.0, 10.0, 100.0], + "means": [[0.55, 0.45], [0.5, 0.5], [0.45, 0.55]], + }, + }, + ), + Reduced( + "rs", + "router", + "router_share", + "Router share", + "species", + { + "categories": ["e-", "gamma"], + "n_experts": 2, + "router_type": "energy", + "rollout": {"e-": [0.7, 0.3], "gamma": [0.2, 0.8]}, + "reference": {"e-": [0.6, 0.4], "gamma": [0.3, 0.7]}, + }, + ), + Reduced( + "ru", + "router", + "unavailable", + "Router unavailable", + "x", + {"note": "router diagnostics unavailable: no router in this run"}, + ), + Reduced( + "g4", + "marginals", + "grouped_hist", + "Grouped (4)", + "x", + { + "edges": [0, 1, 2], + "groups": { + lbl: {"rollout": [1, 2], "reference": [2, 1]} + for lbl in ("a", "b", "c", "d") + }, + "log_y": True, + }, + ), + Reduced( + "sl", + "species", + "single_hist", + "Single (log-x)", + "x", + {"edges": [1, 10, 100], "rollout": [5, 1], "log_x": True, "log_y": True}, + ), + ] + try: + pdfs = _try_render(reduced, tmp_path) + except RuntimeError as e: # LaTeX missing at render time + pytest.skip(f"LaTeX rendering unavailable: {e}") + assert len(pdfs) == len(reduced) + assert all(p.exists() for p in pdfs) + + +def test_render_all_run_gallery_invokes_subprocess(tmp_path: Path, monkeypatch): + calls = [] + monkeypatch.setattr( + render_mod.subprocess, "run", lambda *a, **k: calls.append((a, k)) + ) + reduced = [ + Reduced( + "s", + "species", + "single_hist", + "Single", + "x", + {"edges": [0, 1, 2], "rollout": [5, 1]}, + ) + ] + for r in reduced: + r.save(tmp_path / "reduced" / f"{r.id}.json") + try: + render_mod.render_all( + tmp_path / "reduced", tmp_path / "plots", run_gallery=True + ) + except RuntimeError as e: + pytest.skip(f"LaTeX rendering unavailable: {e}") + + assert len(calls) == 1 + args, kwargs = calls[0] + assert args[0] == ["gallery", "generate", "--source", str(tmp_path / "plots")] + assert kwargs == {"check": True} + + +def test_render_run_glues_condor_run_meta_into_render_all(tmp_path: Path, monkeypatch): + from giant.analysis import condor as condor_mod + + run_dir = tmp_path / "run" + (run_dir / "reduced").mkdir(parents=True) + + merge_calls = [] + monkeypatch.setattr( + condor_mod, "merge_all", lambda rd: merge_calls.append(Path(rd)) + ) + meta = condor_mod.RunMeta( + rollout="rollout.parquet", + reference="reference.parquet", + run_dir=str(run_dir), + title="my-run", + plot_meta={"checkpoint": "ckpt/best.pt"}, + ) + monkeypatch.setattr(condor_mod.RunMeta, "load", classmethod(lambda cls, p: meta)) + + Reduced( + "s", "species", "single_hist", "Single", "x", {"edges": [0, 1], "rollout": [1]} + ).save(run_dir / "reduced" / "s.json") + + try: + pdfs = render_mod.render_run(run_dir) + except RuntimeError as e: + pytest.skip(f"LaTeX rendering unavailable: {e}") + + assert merge_calls == [run_dir] + assert len(pdfs) == 1 + plot_meta = (run_dir / "plots" / "species" / "s.yaml").read_text() + assert "checkpoint" in plot_meta + root_meta = (run_dir / "plots" / "metadata.yaml").read_text() + assert "my-run" in root_meta + + def test_render_one_of_each_kind(tmp_path: Path): reduced = [ Reduced( @@ -90,3 +234,135 @@ def test_render_one_of_each_kind(tmp_path: Path): assert len(pdfs) == len(reduced) assert all(p.exists() for p in pdfs) assert (tmp_path / "plots" / "metadata.yaml").exists() + + +# ── pure-function helpers: no matplotlib figure needed ────────────────── + + +def test_density_zero_total_returns_counts_unchanged(): + counts = np.array([0.0, 0.0, 0.0]) + out = render_mod._density(counts, np.array([0.0, 1.0, 2.0, 3.0])) + np.testing.assert_array_equal(out, counts) + + +def test_density_normalizes_by_total_and_bin_width(): + counts = [1, 3] + edges = np.array([0.0, 2.0, 4.0]) # bin width 2 + out = render_mod._density(counts, edges) + np.testing.assert_allclose(out, np.array([1, 3]) / (4 * 2)) + + +def test_router_summary_disabled_is_off(): + assert render_mod._router_summary({"enabled": False, "type": "energy"}) == "off" + assert render_mod._router_summary({}) == "off" + + +def test_router_summary_enabled_formats_type_and_n_experts(): + cfg = {"enabled": True, "type": "energy", "n_experts": 8} + assert render_mod._router_summary(cfg) == "energy×8" + + +def test_figure_params_v2_basics_and_router_and_epoch(): + mc = { + "stage1_model": { + "hidden_dim": 256, + "n_res_blocks": 4, + "generator": "flow", + "router": {"enabled": True, "type": "energy", "n_experts": 4}, + }, + "conditioning": {"particle": {"type": "physical"}}, + } + run_meta = {"training_epoch": 12, "best_val_loss": 0.123456, "steps": 10} + params = render_mod._figure_params(run_meta | {"model_config": mc}) + assert params == { + "hidden_dim": 256, + "n_res_blocks": 4, + "mode": "flow", + "conditioning": "physical", + "router": "energy×4", + "epoch": 12, + "best_val_loss": 0.1235, + "steps": 10, + } + + +def test_figure_params_v2_wgan_reports_noise_dim_not_steps(): + mc = { + "stage1_model": { + "generator": "wgan", + "wgan": {"noise_dim": 32}, + }, + } + run_meta = {"model_config": mc, "steps": 10} + params = render_mod._figure_params(run_meta) + assert params["mode"] == "wgan" + assert params["noise_dim"] == 32 + assert "steps" not in params + + +def test_figure_params_v2_reports_mode_s2_only_when_it_differs(): + same = { + "stage1_model": {"generator": "flow"}, + "stage2_model": {"generator": "flow"}, + } + assert "mode_s2" not in render_mod._figure_params({"model_config": same}) + + mixed = { + "stage1_model": {"generator": "flow"}, + "stage2_model": {"generator": "wgan"}, + } + params = render_mod._figure_params({"model_config": mixed}) + assert params["mode_s2"] == "wgan" + + +def test_figure_params_old_shape_basics(): + run_meta = { + "model_config": { + "hidden_dim": 128, + "n_blocks": 3, + "mode": "ddpm", + "conditioning": "embedding", + "router": {"enabled": False}, + }, + "training_epoch": 5, + "best_val_loss": 0.5, + "steps": 20, + } + params = render_mod._figure_params(run_meta) + assert params == { + "hidden_dim": 128, + "n_blocks": 3, + "mode": "ddpm", + "conditioning": "embedding", + "router": "off", + "epoch": 5, + "best_val_loss": 0.5, + "steps": 20, + } + + +def test_figure_params_old_shape_wgan_reports_noise_dim_not_steps(): + run_meta = { + "model_config": {"mode": "wgan", "noise_dim": 16}, + "steps": 20, + } + params = render_mod._figure_params(run_meta) + assert params["noise_dim"] == 16 + assert "steps" not in params + + +def test_plot_metadata_includes_note_and_run_meta_parameters(): + r = Reduced( + "u", "router", "unavailable", "Unavailable", "x", {"note": "no router data"} + ) + meta = render_mod._plot_metadata(r, {"title": "run-1", "checkpoint": "ckpt.pt"}) + assert meta["note"] == "no router data" + assert meta["parameters"] == {"checkpoint": "ckpt.pt"} + assert "title" not in meta["parameters"] + + +def test_plot_metadata_omits_parameters_when_run_meta_empty(): + r = Reduced("s", "species", "single_hist", "Single", "x", {"edges": [0, 1]}) + meta = render_mod._plot_metadata(r, {}) + assert "parameters" not in meta + assert "note" not in meta -- 2.39.5 From 9ce7b323248f86bc7ffd332fac0432caba860ebf Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 10 Aug 2026 11:56:36 +0200 Subject: [PATCH 23/30] Fix test_render_all_run_gallery_invokes_subprocess clobbering LaTeX's own subprocess.run MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit render_mod.subprocess is the stdlib subprocess module itself, not a copy — patching .run unconditionally also intercepted the real subprocess.run calls matplotlib's texmanager makes to compile LaTeX during savefig, so those returned the test's fake return value instead of a real CompletedProcess and crashed with AttributeError: 'NoneType' object has no attribute 'stdout' on any environment where render_all runs before the gallery call (i.e. everywhere but this dev machine's warm state that happened to mask it). Only intercept the "gallery generate" call now; everything else passes through to the real subprocess.run. Co-Authored-By: Claude Sonnet 5 --- tests/test_render.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tests/test_render.py b/tests/test_render.py index f5691c1..f1ec9d5 100644 --- a/tests/test_render.py +++ b/tests/test_render.py @@ -98,10 +98,21 @@ def test_render_router_diagnostics_and_edge_cases(tmp_path: Path): def test_render_all_run_gallery_invokes_subprocess(tmp_path: Path, monkeypatch): + # render_mod.subprocess *is* the stdlib subprocess module, so a blanket + # patch of .run would also swallow the real subprocess.run calls + # matplotlib's texmanager makes to compile LaTeX during savefig — only + # intercept the "gallery generate" call itself and pass everything else + # (LaTeX included) through to the real subprocess.run. calls = [] - monkeypatch.setattr( - render_mod.subprocess, "run", lambda *a, **k: calls.append((a, k)) - ) + real_run = render_mod.subprocess.run + + def fake_run(*a, **k): + if a and a[0] and a[0][0] == "gallery": + calls.append((a, k)) + return None + return real_run(*a, **k) + + monkeypatch.setattr(render_mod.subprocess, "run", fake_run) reduced = [ Reduced( "s", -- 2.39.5 From 55332db67aefa336055918bbabb8fc8c04a8406b Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 13:33:09 +0200 Subject: [PATCH 24/30] Bump ruff line-length to 120 and reformat Rejoins lines that only wrapped because they exceeded the old 88-char limit; ruff check and the full test suite (725 passed) are unaffected. --- giant/analysis/catalog.py | 82 ++---- giant/analysis/condor.py | 26 +- giant/analysis/context.py | 31 +- giant/analysis/grouping.py | 8 +- giant/analysis/reduce.py | 9 +- giant/analysis/render.py | 32 +-- giant/analysis/router_gating.py | 45 +-- giant/analysis/runtime_estimate.py | 4 +- giant/analysis/sources.py | 15 +- giant/cli.py | 358 +++++++----------------- giant/config.py | 31 +- giant/data/dataset.py | 4 +- giant/data/loader.py | 32 +-- giant/data/setup_cache.py | 51 +--- giant/data/transforms.py | 84 ++---- giant/geometry.py | 36 +-- giant/materials.py | 35 +-- giant/model/network.py | 252 ++++------------- giant/model/schedule.py | 4 +- giant/particles.py | 13 +- giant/pipeline.py | 100 ++----- giant/rollout.py | 118 +++----- giant/sample.py | 48 +--- giant/training/loop.py | 60 +--- giant/training/metrics.py | 41 +-- giant/training/stage2_inputs.py | 30 +- giant/training/trainers.py | 128 ++------- giant/validate.py | 72 +---- pyproject.toml | 3 + scripts/bump_dataset_version.py | 64 +---- scripts/check_migration_v02_v03.py | 21 +- scripts/create_root_files.py | 38 +-- scripts/dwarf.py | 156 +++-------- scripts/geometry_oracle.py | 4 +- scripts/hparam_scan.py | 13 +- scripts/migrate_geant_steps.py | 56 +--- scripts/profile_analysis_costs.py | 8 +- scripts/steps_to_parquet_parallel.py | 21 +- tests/legacy/network_v02_snapshot.py | 167 +++-------- tests/test_bump_dataset_version.py | 122 ++------ tests/test_catalog.py | 9 +- tests/test_cli_new_run.py | 4 +- tests/test_cli_predict.py | 12 +- tests/test_condor.py | 35 +-- tests/test_config.py | 75 ++--- tests/test_create_root_files.py | 20 +- tests/test_dwarf.py | 4 +- tests/test_geometry.py | 20 +- tests/test_loader.py | 8 +- tests/test_materials.py | 6 +- tests/test_migration_v02_v03.py | 12 +- tests/test_network.py | 55 +--- tests/test_particles.py | 8 +- tests/test_phase2.py | 80 ++---- tests/test_pipeline.py | 36 +-- tests/test_render.py | 23 +- tests/test_rollout.py | 24 +- tests/test_router.py | 72 ++--- tests/test_sample.py | 40 +-- tests/test_setup_cache.py | 17 +- tests/test_steps_to_parquet.py | 12 +- tests/test_steps_to_parquet_parallel.py | 22 +- tests/test_train.py | 84 ++---- tests/test_transforms.py | 50 +--- tests/test_validate.py | 8 +- tests/test_wgan.py | 12 +- 66 files changed, 757 insertions(+), 2413 deletions(-) diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index a4a48d2..458965b 100644 --- a/giant/analysis/catalog.py +++ b/giant/analysis/catalog.py @@ -166,9 +166,7 @@ def _finalize_counts(merged: dict[str, list], key, nbins: int) -> list[int]: return list(merged.get(str(key), [0] * nbins)) -def _np_hist_pair( - r: np.ndarray, t: np.ndarray, nbins: int -) -> tuple[np.ndarray, np.ndarray, np.ndarray]: +def _np_hist_pair(r: np.ndarray, t: np.ndarray, nbins: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """Shared-edge histogram of two small per-event arrays (robust range).""" both = np.concatenate([r, t]) if (len(r) or len(t)) else np.array([0.0, 1.0]) lo, hi = float(np.quantile(both, 0.001)), float(np.quantile(both, 0.999)) @@ -240,9 +238,7 @@ def _marginal_overall_finalize(parts: list[dict], ctx: Context, var: str) -> Red def _energy_group_expr(lf: pl.LazyFrame, edges: np.ndarray) -> pl.Expr: ids, bins = event_energy_bins(lf, edges) - return pl.col("event_id").replace_strict( - ids, bins, default=-1, return_dtype=pl.Int64 - ) + return pl.col("event_id").replace_strict(ids, bins, default=-1, return_dtype=pl.Int64) def _marginal_grouped_partial(b: Bundle, var: str, axis: str) -> dict: @@ -265,9 +261,7 @@ def _marginal_grouped_partial(b: Bundle, var: str, axis: str) -> dict: } -def _marginal_grouped_finalize( - parts: list[dict], ctx: Context, var: str, axis: str -) -> Reduced: +def _marginal_grouped_finalize(parts: list[dict], ctx: Context, var: str, axis: str) -> Reduced: label, _ = _var(var) edges = _marginal_edges(ctx, var) nb = len(edges) - 1 @@ -317,9 +311,7 @@ def _event_scalar_partial(b: Bundle, col: str, use_all: bool) -> dict: return {"r": r.tolist(), "t": t.tolist()} -def _event_scalar_finalize( - parts: list[dict], ctx: Context, spec_id: str, title: str, xlabel: str -) -> Reduced: +def _event_scalar_finalize(parts: list[dict], ctx: Context, spec_id: str, title: str, xlabel: str) -> Reduced: r = np.concatenate([np.asarray(p["r"], dtype=float) for p in parts]) t = np.concatenate([np.asarray(p["t"], dtype=float) for p in parts]) edges, rc, tc = _np_hist_pair(r, t, ctx.n_marginal_bins) @@ -488,9 +480,7 @@ def _leakage_partial(b: Bundle) -> dict: def _leakage_finalize(parts: list[dict], ctx: Context) -> Reduced: frac = np.concatenate([np.asarray(p["frac"], dtype=float) for p in parts]) - edges = np.linspace( - 0.0, max(float(frac.max()) if len(frac) else 1.0, 1e-3), ctx.n_marginal_bins + 1 - ) + edges = np.linspace(0.0, max(float(frac.max()) if len(frac) else 1.0, 1e-3), ctx.n_marginal_bins + 1) counts = np.histogram(frac, edges)[0] return Reduced( id="leakage_fraction", @@ -521,18 +511,8 @@ def _sec_frames(b: Bundle): def _sec_count_per_event_partial(b: Bundle) -> dict: r_sec, t_sec = _sec_frames(b) - r = ( - r_sec.group_by("event_id") - .agg(pl.len().alias("n")) - .collect(engine="streaming")["n"] - .to_numpy() - ) - t = ( - t_sec.group_by("event_id") - .agg(pl.len().alias("n")) - .collect(engine="streaming")["n"] - .to_numpy() - ) + r = r_sec.group_by("event_id").agg(pl.len().alias("n")).collect(engine="streaming")["n"].to_numpy() + t = t_sec.group_by("event_id").agg(pl.len().alias("n")).collect(engine="streaming")["n"].to_numpy() return {"r": r.tolist(), "t": t.tolist()} @@ -568,9 +548,7 @@ def _sec_count_per_species_partial(b: Bundle) -> dict: def _sec_count_per_species_finalize(parts: list[dict], ctx: Context) -> Reduced: r = sum_merge([p["r"] for p in parts]) t = sum_merge([p["t"] for p in parts]) - keys = sorted(set(r) | set(t), key=lambda k: -(r.get(k, 0) + t.get(k, 0)))[ - : len(ctx.top_pdgs) - ] + keys = sorted(set(r) | set(t), key=lambda k: -(r.get(k, 0) + t.get(k, 0)))[: len(ctx.top_pdgs)] return Reduced( id="sec_count_per_species", family="secondaries", @@ -617,11 +595,9 @@ def _sec_energy_finalize(parts: list[dict], ctx: Context) -> Reduced: def _sec_cos_angle_partial(b: Bundle) -> dict: edges = np.linspace(-1.0, 1.0, b.ctx.n_sec_bins + 1) - cos = ( - pl.col("sdx") * pl.col("axis_x") - + pl.col("sdy") * pl.col("axis_y") - + pl.col("sdz") * pl.col("axis_z") - ).clip(-1.0, 1.0) + cos = (pl.col("sdx") * pl.col("axis_x") + pl.col("sdy") * pl.col("axis_y") + pl.col("sdz") * pl.col("axis_z")).clip( + -1.0, 1.0 + ) def _side(sec_lf: pl.LazyFrame, steps_lf: pl.LazyFrame) -> dict[str, list[int]]: ea = entry_axis(steps_lf) @@ -659,15 +635,13 @@ _router_gating_partial, _router_gating_finalize = _unchunkable( lambda b: compute_router_gating(b.checkpoint, b.r_phys, b.t_phys) ) _router_share_pdg_partial, _router_share_pdg_finalize = _unchunkable( - lambda b: compute_router_share_by_pdg( - b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs - ) + lambda b: compute_router_share_by_pdg(b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs) ) _router_share_process_partial, _router_share_process_finalize = _unchunkable( lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys) ) -_type_embedding_l1_distance_partial, _type_embedding_l1_distance_finalize = ( - _unchunkable(lambda b: compute_type_embedding_l1_distance(b.type_embedding_l1_dist)) +_type_embedding_l1_distance_partial, _type_embedding_l1_distance_finalize = _unchunkable( + lambda b: compute_type_embedding_l1_distance(b.type_embedding_l1_dist) ) @@ -689,9 +663,7 @@ def build_catalog() -> list[PlotSpec]: f"marginal_{var}", "marginals", compute_partial=lambda b, v=var: _marginal_overall_partial(b, v), - finalize=lambda parts, ctx, v=var: _marginal_overall_finalize( - parts, ctx, v - ), + finalize=lambda parts, ctx, v=var: _marginal_overall_finalize(parts, ctx, v), ) ) for axis in GROUPING_AXES: @@ -699,12 +671,8 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( f"marginal_{var}_by_{axis}", "marginals", - compute_partial=lambda b, v=var, a=axis: _marginal_grouped_partial( - b, v, a - ), - finalize=lambda parts, ctx, v=var, a=axis: ( - _marginal_grouped_finalize(parts, ctx, v, a) - ), + compute_partial=lambda b, v=var, a=axis: _marginal_grouped_partial(b, v, a), + finalize=lambda parts, ctx, v=var, a=axis: _marginal_grouped_finalize(parts, ctx, v, a), ) ) @@ -712,9 +680,7 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( "event_total_edep", "event", - compute_partial=lambda b: _event_scalar_partial( - b, "total_edep", use_all=True - ), + compute_partial=lambda b: _event_scalar_partial(b, "total_edep", use_all=True), finalize=lambda parts, ctx: _event_scalar_finalize( parts, ctx, @@ -732,9 +698,7 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( "event_mean_length", "event", - compute_partial=lambda b: _event_scalar_partial( - b, "mean_length", use_all=False - ), + compute_partial=lambda b: _event_scalar_partial(b, "mean_length", use_all=False), finalize=lambda parts, ctx: _event_scalar_finalize( parts, ctx, @@ -746,9 +710,7 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( "event_n_steps", "event", - compute_partial=lambda b: _event_scalar_partial( - b, "n_steps", use_all=False - ), + compute_partial=lambda b: _event_scalar_partial(b, "n_steps", use_all=False), finalize=lambda parts, ctx: _event_scalar_finalize( parts, ctx, @@ -773,9 +735,7 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( "shower_transverse", "shower", - compute_partial=lambda b: _profile_partial( - b, transverse_expr, "transverse_edges" - ), + compute_partial=lambda b: _profile_partial(b, transverse_expr, "transverse_edges"), finalize=lambda parts, ctx: _profile_finalize( parts, ctx, diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 9cd1c64..f512621 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -161,9 +161,7 @@ class RunMeta: return cls(**json.loads(Path(path).read_text())) -def _rows_per_chunk( - rollout: str | Path, reference: str | Path, n_chunks: int -) -> list[int]: +def _rows_per_chunk(rollout: str | Path, reference: str | Path, n_chunks: int) -> list[int]: """Rollout+reference row count of each ``event_id % n_chunks`` chunk. One cheap streaming ``group_by`` per side (just the ``event_id`` column) — @@ -268,8 +266,7 @@ def compute_reduced( effective_n = n_chunks if spec.chunkable else 1 if not (0 <= chunk_index < effective_n): raise ValueError( - f"{spec_id}: chunk_index={chunk_index} out of range for " - f"n_chunks={effective_n} (chunkable={spec.chunkable})" + f"{spec_id}: chunk_index={chunk_index} out of range for n_chunks={effective_n} (chunkable={spec.chunkable})" ) bundle = Bundle.open( rollout, @@ -327,10 +324,7 @@ def merge_one(spec_id: str, run_dir: str | Path) -> Path: effective_n = meta.n_chunks if spec.chunkable else 1 partial_dir = run_path / "reduced_partial" - found = { - p.chunk: p - for p in (Partial.load(jf) for jf in partial_dir.glob(f"{spec_id}__*.json")) - } + found = {p.chunk: p for p in (Partial.load(jf) for jf in partial_dir.glob(f"{spec_id}__*.json"))} missing = sorted(set(range(effective_n)) - set(found)) if missing: raise FileNotFoundError( @@ -375,11 +369,7 @@ exec {giant_exe} analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir} def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> str: - reqs_attrs = ( - "+RemoteJob = True\n" - if cfg.remote - else "requirements = TARGET.ProvidesETPResources\n" - ) + reqs_attrs = "+RemoteJob = True\n" if cfg.remote else "requirements = TARGET.ProvidesETPResources\n" return ( "universe = docker\n" f"docker_image = {cfg.docker_image}\n" @@ -399,9 +389,7 @@ def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> st ) -def _job_walltimes( - run_dir: Path, ids: list[str], n_chunks: int -) -> list[tuple[str, int, int]]: +def _job_walltimes(run_dir: Path, ids: list[str], n_chunks: int) -> list[tuple[str, int, int]]: """``(spec_id, chunk, walltime_s)`` for every job, sized from ``run_meta.json``. Row counts come from ``prep``'s ``RunMeta.rows_per_chunk``/``total_rows``; @@ -477,9 +465,7 @@ def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path: (run_dir / "reduced_partial").mkdir(parents=True, exist_ok=True) wrapper = run_dir / "run_compute.sh" - wrapper.write_text( - _WRAPPER.format(repo_dir=cfg.repo_dir, giant_exe=giant_exe, run_dir=run_dir) - ) + wrapper.write_text(_WRAPPER.format(repo_dir=cfg.repo_dir, giant_exe=giant_exe, run_dir=run_dir)) wrapper.chmod(0o755) jobs = _job_walltimes(run_dir, ids, cfg.n_chunks) diff --git a/giant/analysis/context.py b/giant/analysis/context.py index f8cd84b..090ead5 100644 --- a/giant/analysis/context.py +++ b/giant/analysis/context.py @@ -74,9 +74,7 @@ def _row_subsample(lf: pl.LazyFrame, sample_rows: int, seed: int) -> pl.LazyFram return lf.filter((pl.col("pre_E").hash(seed=seed) % 2**32) < threshold) -def _combined_quantiles( - r_vals: np.ndarray, t_vals: np.ndarray, lo_q: float, hi_q: float -) -> tuple[float, float]: +def _combined_quantiles(r_vals: np.ndarray, t_vals: np.ndarray, lo_q: float, hi_q: float) -> tuple[float, float]: """Robust (lo_q, hi_q) range over the union of two value samples.""" both = np.concatenate([r_vals, t_vals]) lo, hi = float(np.quantile(both, lo_q)), float(np.quantile(both, hi_q)) @@ -104,31 +102,15 @@ def build_context( # Ranged marginal variables: robust ranges over a shared row subsample. exprs = [e.alias(n) for n, (_, e) in RANGED_VARS.items()] - r_s = ( - _row_subsample(r_lf, sample_rows, seed) - .select(exprs) - .collect(engine="streaming") - ) - t_s = ( - _row_subsample(t_lf, sample_rows, seed) - .select(exprs) - .collect(engine="streaming") - ) + r_s = _row_subsample(r_lf, sample_rows, seed).select(exprs).collect(engine="streaming") + t_s = _row_subsample(t_lf, sample_rows, seed).select(exprs).collect(engine="streaming") var_ranges = { - name: _combined_quantiles( - r_s[name].to_numpy(), t_s[name].to_numpy(), _LO_Q, _HI_Q - ) - for name in RANGED_VARS + name: _combined_quantiles(r_s[name].to_numpy(), t_s[name].to_numpy(), _LO_Q, _HI_Q) for name in RANGED_VARS } # Energy-bin edges from exact per-event incident energies (cheap group_by). def _incident(lf: pl.LazyFrame) -> np.ndarray: - return ( - lf.group_by("event_id") - .agg(pl.col("pre_E").max()) - .collect(engine="streaming")["pre_E"] - .to_numpy() - ) + return lf.group_by("event_id").agg(pl.col("pre_E").max()).collect(engine="streaming")["pre_E"].to_numpy() r_inc, t_inc = _incident(r_lf), _incident(t_lf) energy_edges = energy_bin_edges(np.concatenate([r_inc, t_inc]), n_energy_bins) @@ -145,8 +127,7 @@ def build_context( ) top_pdgs = [int(x) for x in pdg_counts["pdg"].to_list()[:top_k_pdg]] materials = sorted( - set(_counts(r_lf, "material")["material"].to_list()) - | set(_counts(t_lf, "material")["material"].to_list()) + set(_counts(r_lf, "material")["material"].to_list()) | set(_counts(t_lf, "material")["material"].to_list()) ) # Shower depth / transverse ranges from a subsampled proxy. diff --git a/giant/analysis/grouping.py b/giant/analysis/grouping.py index e5ec095..bc42005 100644 --- a/giant/analysis/grouping.py +++ b/giant/analysis/grouping.py @@ -68,9 +68,7 @@ def energy_bin_edges(incident_E: np.ndarray, n_bins: int = 4) -> np.ndarray: def energy_bin_labels(edges: np.ndarray) -> list[str]: """``E in [lo, hi)`` labels for each bin defined by ``edges`` (MeV).""" - return [ - f"E in [{edges[i]:.3g}, {edges[i + 1]:.3g}) MeV" for i in range(len(edges) - 1) - ] + return [f"E in [{edges[i]:.3g}, {edges[i + 1]:.3g}) MeV" for i in range(len(edges) - 1)] def digitize_expr(value: pl.Expr, edges: np.ndarray) -> pl.Expr: @@ -88,9 +86,7 @@ def digitize_expr(value: pl.Expr, edges: np.ndarray) -> pl.Expr: return idx.clip(0, n_bins - 1) -def event_energy_bins( - lf: pl.LazyFrame, edges: np.ndarray -) -> tuple[np.ndarray, np.ndarray]: +def event_energy_bins(lf: pl.LazyFrame, edges: np.ndarray) -> tuple[np.ndarray, np.ndarray]: """Per-event incident-energy bin: ``(event_ids, bin_idx)`` numpy arrays. Incident energy is ``max(pre_E)`` per event (the primary). One bounded diff --git a/giant/analysis/reduce.py b/giant/analysis/reduce.py index 15089f6..b050563 100644 --- a/giant/analysis/reduce.py +++ b/giant/analysis/reduce.py @@ -142,9 +142,7 @@ def attach_entry_axis(lf: pl.LazyFrame, entry: pl.DataFrame) -> pl.LazyFrame: """ ids = entry["event_id"].to_numpy() return lf.with_columns( - pl.col("event_id") - .replace_strict(ids, entry[col].to_numpy(), return_dtype=pl.Float64) - .alias(col) + pl.col("event_id").replace_strict(ids, entry[col].to_numpy(), return_dtype=pl.Float64).alias(col) for col in _ENTRY_AXIS_COLS ) @@ -254,10 +252,7 @@ def leakage_fraction(lf: pl.LazyFrame) -> np.ndarray: lf.group_by("event_id") .agg( pl.col("edep").sum().alias("deposited"), - pl.col("pre_E") - .filter(pl.col("termination_reason") == TERM_ESCAPED) - .sum() - .alias("escaped"), + pl.col("pre_E").filter(pl.col("termination_reason") == TERM_ESCAPED).sum().alias("escaped"), ) .collect(engine="streaming") ) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index 862e023..f5f57ea 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -139,9 +139,7 @@ def _render_overlay(r: Reduced, params: dict): def _render_single(r: Reduced, params: dict): edges = np.asarray(r.payload["edges"]) fig, ax = ps.new_figure("thesis-single", title=r.title, params=params) - ax.stairs( - _density(r.payload["rollout"], edges), edges, label=_SERIES_LABELS["rollout"] - ) + ax.stairs(_density(r.payload["rollout"], edges), edges, label=_SERIES_LABELS["rollout"]) if r.payload.get("log_y"): ax.set_yscale("log") if r.payload.get("log_x"): @@ -187,9 +185,7 @@ def _render_profile(r: Reduced, params: dict): mean = np.asarray(r.payload[f"{key}_mean"]) std = np.asarray(r.payload[f"{key}_std"]) (line,) = ax.plot(centers, mean, label=_SERIES_LABELS[key]) - ax.fill_between( - centers, mean - std, mean + std, alpha=0.2, color=line.get_color() - ) + ax.fill_between(centers, mean - std, mean + std, alpha=0.2, color=line.get_color()) ax.set_xlabel(r.xlabel) ax.set_ylabel(r.payload.get("ylabel", "mean deposited energy [MeV]")) ps.style_legend(ax, title="source") @@ -201,9 +197,7 @@ def _render_bar(r: Reduced, params: dict): x = np.arange(len(labels)) width = 0.4 fig, ax = ps.new_figure("thesis-single", title=r.title, params=params) - ax.bar( - x - width / 2, r.payload["reference"], width, label=_SERIES_LABELS["reference"] - ) + ax.bar(x - width / 2, r.payload["reference"], width, label=_SERIES_LABELS["reference"]) ax.bar(x + width / 2, r.payload["rollout"], width, label=_SERIES_LABELS["rollout"]) ax.set_xticks(x) ax.set_xticklabels(labels, rotation=45, ha="right") @@ -215,9 +209,7 @@ def _render_bar(r: Reduced, params: dict): def _render_router_gating(r: Reduced, params: dict): n_experts = r.payload["n_experts"] log_x = r.payload.get("log_x", False) - fig, axes = ps.new_figure( - "slide-16x9", title=r.title, params=params, nrows=1, ncols=2, squeeze=False - ) + fig, axes = ps.new_figure("slide-16x9", title=r.title, params=params, nrows=1, ncols=2, squeeze=False) flat = axes.ravel() for ax, key in zip(flat, ("rollout", "reference")): side = r.payload.get(key, {}) @@ -226,9 +218,7 @@ def _render_router_gating(r: Reduced, params: dict): if len(centers) and means.size: cum = np.zeros(len(centers)) for i in range(n_experts): - ax.fill_between( - centers, cum, cum + means[:, i], alpha=0.7, label=f"expert {i}" - ) + ax.fill_between(centers, cum, cum + means[:, i], alpha=0.7, label=f"expert {i}") cum = cum + means[:, i] if log_x: ax.set_xscale("log") @@ -347,9 +337,7 @@ def render_all( families.add(r.family) fig = render(r, run_meta) ps.savefig(fig, str(family_dir / r.id), formats=("pdf",)) - (family_dir / f"{r.id}.yaml").write_text( - yaml.safe_dump(_plot_metadata(r, run_meta), sort_keys=False) - ) + (family_dir / f"{r.id}.yaml").write_text(yaml.safe_dump(_plot_metadata(r, run_meta), sort_keys=False)) pdfs.append(family_dir / f"{r.id}.pdf") import matplotlib.pyplot as plt @@ -370,9 +358,7 @@ def render_all( ) for fam in families: (out_dir / fam / "metadata.yaml").write_text( - yaml.safe_dump( - {"title": fam, "description": f"{fam} plots."}, sort_keys=False - ) + yaml.safe_dump({"title": fam, "description": f"{fam} plots."}, sort_keys=False) ) if run_gallery: @@ -400,6 +386,4 @@ def render_run(run_dir: str | Path, *, run_gallery: bool = False) -> list[Path]: "reference": meta.reference, **meta.plot_meta, } - return render_all( - run_dir / "reduced", run_dir / "plots", run_meta, run_gallery=run_gallery - ) + return render_all(run_dir / "reduced", run_dir / "plots", run_meta, run_gallery=run_gallery) diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index 491a433..a13c287 100644 --- a/giant/analysis/router_gating.py +++ b/giant/analysis/router_gating.py @@ -95,9 +95,7 @@ def load_router(checkpoint: str | Path) -> _RouterHandle | None: # New nested shape (has a "stage1_model" key) vs. a v0.2 checkpoint's # flat model_config. router_cfg = ( - (model_cfg.get("stage1_model") or {}).get("router") - if "stage1_model" in model_cfg - else model_cfg.get("router") + (model_cfg.get("stage1_model") or {}).get("router") if "stage1_model" in model_cfg else model_cfg.get("router") ) if not router_cfg or not router_cfg.get("enabled"): return None @@ -124,9 +122,7 @@ def load_router(checkpoint: str | Path) -> _RouterHandle | None: ) -def _subsample( - lf: pl.LazyFrame, n: int, seed: int, extra_cols: tuple = () -) -> pl.DataFrame: +def _subsample(lf: pl.LazyFrame, n: int, seed: int, extra_cols: tuple = ()) -> pl.DataFrame: total = lf.select(pl.len()).collect(engine="streaming").item() if total > n: threshold = int(n / total * 2**32) @@ -134,9 +130,7 @@ def _subsample( return lf.select(*_COLS, *extra_cols).collect(engine="streaming") -def _gate_for_df( - handle: _RouterHandle, df: pl.DataFrame -) -> tuple[pl.DataFrame, np.ndarray]: +def _gate_for_df(handle: _RouterHandle, df: pl.DataFrame) -> tuple[pl.DataFrame, np.ndarray]: """(filtered df, gate_weights) for rows in ``df`` with a known pdg/material. Rows whose species or material never appeared in the checkpoint's @@ -160,13 +154,9 @@ def _gate_for_df( df = df.filter(pl.Series(known, dtype=pl.Boolean)) data = { - "pre_pos": np.column_stack( - [df["pre_x"].to_numpy(), df["pre_y"].to_numpy(), df["pre_z"].to_numpy()] - ), + "pre_pos": np.column_stack([df["pre_x"].to_numpy(), df["pre_y"].to_numpy(), df["pre_z"].to_numpy()]), "pre_E": df["pre_E"].to_numpy(), - "pre_dir": np.column_stack( - [df["pre_dx"].to_numpy(), df["pre_dy"].to_numpy(), df["pre_dz"].to_numpy()] - ), + "pre_dir": np.column_stack([df["pre_dx"].to_numpy(), df["pre_dy"].to_numpy(), df["pre_dz"].to_numpy()]), "layer_id": df["layer_id"].to_numpy(), "pdg": df["pdg"].to_numpy(), "material": df["material"].to_numpy(), @@ -180,9 +170,7 @@ def _gate_for_df( material_conditioning=handle.material_conditioning, ) with torch.no_grad(): - gate = handle.router.gate( - torch.from_numpy(cond_cont).float(), torch.from_numpy(cond_cat).long() - ).numpy() + gate = handle.router.gate(torch.from_numpy(cond_cont).float(), torch.from_numpy(cond_cat).long()).numpy() return df, gate @@ -205,9 +193,7 @@ def _quantile_bins(x: np.ndarray, gate: np.ndarray, n_bins: int) -> dict: return {"centers": centers[valid].tolist(), "means": means[valid].tolist()} -def _top1_shares( - categories: np.ndarray, idx: np.ndarray, order: list, n_experts: int -) -> dict[str, list[float]]: +def _top1_shares(categories: np.ndarray, idx: np.ndarray, order: list, n_experts: int) -> dict[str, list[float]]: """Fraction of each category's rows hard-dispatched to each expert. Uses `Router.top1` (argmax), not the soft `gate` mean — grouped top-1 @@ -227,10 +213,7 @@ def _top1_shares( return shares -_NOTE_NOT_MOE = ( - "checkpoint has no enabled MoE router (model.router.enabled is " - "false/absent) — nothing to show" -) +_NOTE_NOT_MOE = "checkpoint has no enabled MoE router (model.router.enabled is false/absent) — nothing to show" _TITLES = { "router_gating": "Router gating (mixture-of-experts decision boundaries)", @@ -266,9 +249,7 @@ def compute_router_gating( df = _subsample(lf, _SAMPLE_ROWS, seed) df, gate = _gate_for_df(handle, df) x = df["pre_E"].to_numpy() - sides[name] = ( - _quantile_bins(x, gate, _N_BINS) if len(x) else {"centers": [], "means": []} - ) + sides[name] = _quantile_bins(x, gate, _N_BINS) if len(x) else {"centers": [], "means": []} return Reduced( id="router_gating", @@ -304,9 +285,7 @@ def compute_router_share_by_pdg( df, gate = _gate_for_df(handle, df) if len(df): idx = gate.argmax(axis=1) - shares = _top1_shares( - df["pdg"].to_numpy(), idx, top_pdgs, handle.router.n_experts - ) + shares = _top1_shares(df["pdg"].to_numpy(), idx, top_pdgs, handle.router.n_experts) else: shares = {str(p): [0.0] * handle.router.n_experts for p in top_pdgs} sides[name] = {labels[i]: shares[str(p)] for i, p in enumerate(top_pdgs)} @@ -351,9 +330,7 @@ def compute_router_share_by_process( counts = df["process"].value_counts().sort("count", descending=True) order = counts["process"].to_list()[:top_k] idx = gate.argmax(axis=1) - shares = _top1_shares( - df["process"].to_numpy(), idx, order, handle.router.n_experts - ) + shares = _top1_shares(df["process"].to_numpy(), idx, order, handle.router.n_experts) else: order, shares = [], {} diff --git a/giant/analysis/runtime_estimate.py b/giant/analysis/runtime_estimate.py index f5279f7..7f6cc37 100644 --- a/giant/analysis/runtime_estimate.py +++ b/giant/analysis/runtime_estimate.py @@ -54,9 +54,7 @@ _FIXED_OVERHEAD_S = 60.0 # scan. Calibrated from the 3 real router jobs' observed wall times (119, 66, # 124s) — max minus _FIXED_OVERHEAD_S, on top of it. _ROUTER_FIXED_S = 64.0 -_ROUTER_IDS = frozenset( - {"router_gating", "router_share_by_pdg", "router_share_by_process"} -) +_ROUTER_IDS = frozenset({"router_gating", "router_share_by_pdg", "router_share_by_process"}) # Conservative fallback for any catalog id not in _COST_MODEL (e.g. a plot # added after the last calibration run) — the most expensive fitted per-row diff --git a/giant/analysis/sources.py b/giant/analysis/sources.py index 2248c90..abedc74 100644 --- a/giant/analysis/sources.py +++ b/giant/analysis/sources.py @@ -93,10 +93,7 @@ def _check_rollout_metadata(path: Path) -> None: metadata = pq.read_schema(path).metadata or {} coord = metadata.get(PREDICT_COORD_METADATA_KEY.encode()) if coord is not None and coord.decode() != ROLLOUT_COORD_VALUE: - raise ValueError( - f"{path} is not a rollout file (coord={coord.decode()!r}); " - "expected `giant rollout` output" - ) + raise ValueError(f"{path} is not a rollout file (coord={coord.decode()!r}); expected `giant rollout` output") def open_side(source: str | Path | pl.LazyFrame, side: Side) -> pl.LazyFrame: @@ -121,11 +118,7 @@ def open_side(source: str | Path | pl.LazyFrame, side: Side) -> pl.LazyFrame: else: # The reference (a rollout's seed `dataset`) may be a directory of # parquet shards rather than a single file — scan them all. - lf = ( - pl.scan_parquet(str(path / "**/*.parquet")) - if path.is_dir() - else pl.scan_parquet(path) - ) + lf = pl.scan_parquet(str(path / "**/*.parquet")) if path.is_dir() else pl.scan_parquet(path) return lf.with_columns(pl.col("pdg").cast(pl.Int64)) @@ -137,9 +130,7 @@ def physical_steps(lf: pl.LazyFrame, side: Side) -> pl.LazyFrame: """ if side is Side.reference: return lf - return lf.filter( - ~pl.col("termination_reason").is_in(list(SYNTHETIC_TERMINATION_REASONS)) - ) + return lf.filter(~pl.col("termination_reason").is_in(list(SYNTHETIC_TERMINATION_REASONS))) def secondaries(lf: pl.LazyFrame, side: Side) -> pl.LazyFrame: diff --git a/giant/cli.py b/giant/cli.py index d4a5229..ee49c1d 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -65,9 +65,7 @@ def _router_total_experts(router_cfg: dict) -> int: """ if router_cfg.get("type") == "composed": axis_counts = { - m.group(1): int(v) - for k, v in router_cfg.items() - if (m := re.match(r"^axis(\d+)_n_experts$", k)) + m.group(1): int(v) for k, v in router_cfg.items() if (m := re.match(r"^axis(\d+)_n_experts$", k)) } return math.prod(axis_counts.values()) if axis_counts else 1 return int(router_cfg.get("n_experts", 1)) @@ -104,11 +102,7 @@ def _ddpm_steps(model_cfg: dict, stage: str) -> int: def _particle_type_other_policy(model_cfg: dict) -> str: - return ( - _stage_cfg(model_cfg, "stage2") - .get("particle_type", {}) - .get("other_policy", "sample") - ) + return _stage_cfg(model_cfg, "stage2").get("particle_type", {}).get("other_policy", "sample") def _load_pdg_topn_map(ckpt: dict): @@ -129,9 +123,7 @@ def _load_mat_topn_map(ckpt: dict): return topnmap_from_json(raw, axis="material") if raw is not None else None -def _batch_size_estimate_dims( - model_cfg: dict, training: bool, stage: str = "stage1" -) -> tuple[int, int]: +def _batch_size_estimate_dims(model_cfg: dict, training: bool, stage: str = "stage1") -> tuple[int, int]: """Pick the (hidden_dim, n_blocks) that dominate per-call activation memory. `model_cfg` is either the new nested shape (has a `f"{stage}_model"` key @@ -336,14 +328,10 @@ def _load_model_weights( @app.command() def train( - data: Annotated[ - Path, typer.Argument(help="Parquet file or directory of parquet files") - ], + data: Annotated[Path, typer.Argument(help="Parquet file or directory of parquet files")], config: Annotated[ Optional[Path], - typer.Option( - "--config", "-c", help="TOML config file (overridden by explicit flags)" - ), + typer.Option("--config", "-c", help="TOML config file (overridden by explicit flags)"), ] = None, mode: Annotated[ Optional[Mode], @@ -355,8 +343,7 @@ def train( typer.Option( "--batch-size", "-b", - help="Integer, or 'auto' to estimate from free GPU memory " - "(cuda devices only)", + help="Integer, or 'auto' to estimate from free GPU memory (cuda devices only)", ), ] = None, lr: Annotated[Optional[float], typer.Option("--lr", "-l")] = None, @@ -372,17 +359,13 @@ def train( "alongside the raw weights in checkpoints (0 disables; default: 0.9999)", ), ] = None, - warmup_epochs: Annotated[ - Optional[int], typer.Option("--warmup-epochs", "-w") - ] = None, + warmup_epochs: Annotated[Optional[int], typer.Option("--warmup-epochs", "-w")] = None, hidden_dim: Annotated[Optional[int], typer.Option("--hidden-dim", "-H")] = None, n_blocks: Annotated[Optional[int], typer.Option("--n-blocks", "-n")] = None, emb_dim: Annotated[Optional[int], typer.Option("--emb-dim", "-E")] = None, dropout: Annotated[ Optional[float], - typer.Option( - "--dropout", "-d", help="Dropout probability in ResBlocks (default: 0.1)" - ), + typer.Option("--dropout", "-d", help="Dropout probability in ResBlocks (default: 0.1)"), ] = None, stage1_generator: Annotated[ Optional[Mode], @@ -402,9 +385,7 @@ def train( ] = None, stage1_n_res_blocks: Annotated[ Optional[int], - typer.Option( - "--stage1-n-res-blocks", help="Overrides --n-blocks for stage 1 only" - ), + typer.Option("--stage1-n-res-blocks", help="Overrides --n-blocks for stage 1 only"), ] = None, stage1_dropout: Annotated[ Optional[float], @@ -452,8 +433,7 @@ def train( Optional[int], typer.Option( "--stage2-context-dim", - help="Width of the projected stage-1 outcome fed into stage 2's " - "conditioning (default: 64)", + help="Width of the projected stage-1 outcome fed into stage 2's conditioning (default: 64)", ), ] = None, stage2_stage1_context: Annotated[ @@ -485,13 +465,9 @@ def train( ] = None, router_type: Annotated[ Optional[str], - typer.Option( - "--router-type", help="Router implementation name (see ROUTER_REGISTRY)" - ), - ] = None, - n_experts: Annotated[ - Optional[int], typer.Option("--n-experts", help="Number of routed experts") + typer.Option("--router-type", help="Router implementation name (see ROUTER_REGISTRY)"), ] = None, + n_experts: Annotated[Optional[int], typer.Option("--n-experts", help="Number of routed experts")] = None, router_axis: Annotated[ Optional[list[str]], typer.Option( @@ -506,32 +482,28 @@ def train( Optional[int], typer.Option( "--n-critic", - help="WGAN-GP (--mode wgan only): critic updates per generator " - "update (default: 5)", + help="WGAN-GP (--mode wgan only): critic updates per generator update (default: 5)", ), ] = None, gp_weight: Annotated[ Optional[float], typer.Option( "--gp-weight", - help="WGAN-GP (--mode wgan only): gradient-penalty coefficient " - "(default: 10.0)", + help="WGAN-GP (--mode wgan only): gradient-penalty coefficient (default: 10.0)", ), ] = None, noise_dim: Annotated[ Optional[int], typer.Option( "--noise-dim", - help="WGAN (--mode wgan only): generator input noise-vector " - "width (default: 64)", + help="WGAN (--mode wgan only): generator input noise-vector width (default: 64)", ), ] = None, critic_lr: Annotated[ Optional[float], typer.Option( "--critic-lr", - help="WGAN-GP (--mode wgan only): critic learning rate " - "(default: same as --lr)", + help="WGAN-GP (--mode wgan only): critic learning rate (default: same as --lr)", ), ] = None, stage1_n_critic: Annotated[ @@ -540,21 +512,15 @@ def train( ] = None, stage1_gp_weight: Annotated[ Optional[float], - typer.Option( - "--stage1-gp-weight", help="Overrides --gp-weight for stage 1 only" - ), + typer.Option("--stage1-gp-weight", help="Overrides --gp-weight for stage 1 only"), ] = None, stage1_noise_dim: Annotated[ Optional[int], - typer.Option( - "--stage1-noise-dim", help="Overrides --noise-dim for stage 1 only" - ), + typer.Option("--stage1-noise-dim", help="Overrides --noise-dim for stage 1 only"), ] = None, stage1_critic_lr: Annotated[ Optional[float], - typer.Option( - "--stage1-critic-lr", help="Overrides --critic-lr for stage 1 only" - ), + typer.Option("--stage1-critic-lr", help="Overrides --critic-lr for stage 1 only"), ] = None, stage2_n_critic: Annotated[ Optional[int], @@ -562,9 +528,7 @@ def train( ] = None, stage2_gp_weight: Annotated[ Optional[float], - typer.Option( - "--stage2-gp-weight", help="Overrides --gp-weight for stage 2 only" - ), + typer.Option("--stage2-gp-weight", help="Overrides --gp-weight for stage 2 only"), ] = None, stage2_noise_dim: Annotated[ Optional[int], @@ -576,13 +540,9 @@ def train( ] = None, stage2_critic_lr: Annotated[ Optional[float], - typer.Option( - "--stage2-critic-lr", help="Overrides --critic-lr for stage 2 only" - ), - ] = None, - val_fraction: Annotated[ - Optional[float], typer.Option("--val-fraction", "-f") + typer.Option("--stage2-critic-lr", help="Overrides --critic-lr for stage 2 only"), ] = None, + val_fraction: Annotated[Optional[float], typer.Option("--val-fraction", "-f")] = None, seed: Annotated[ Optional[int], typer.Option("--seed", "-s", help="Random seed for reproducibility"), @@ -608,15 +568,12 @@ def train( Optional[int], typer.Option( "--max-val-batches", - help="Cap the per-epoch val-loss pass to N batches (0 = full " - "val set every epoch; default: 200)", + help="Cap the per-epoch val-loss pass to N batches (0 = full val set every epoch; default: 200)", ), ] = None, shuffle_buffer: Annotated[ int, - typer.Option( - "--shuffle-buffer", "-B", help="Rows held in RAM per worker for shuffling" - ), + typer.Option("--shuffle-buffer", "-B", help="Rows held in RAM per worker for shuffling"), ] = 65536, cache_setup: Annotated[ bool, @@ -660,8 +617,7 @@ def train( Optional[bool], typer.Option( "--wandb/--no-wandb", - help="Log per-epoch training metrics to Weights & Biases " - "(requires `uv sync --extra wandb`)", + help="Log per-epoch training metrics to Weights & Biases (requires `uv sync --extra wandb`)", ), ] = None, wandb_project: Annotated[ @@ -692,8 +648,7 @@ def train( batch_size_value = int(batch_size) except ValueError: typer.echo( - f"error: --batch-size must be an integer or 'auto', " - f"got {batch_size!r}", + f"error: --batch-size must be an integer or 'auto', got {batch_size!r}", err=True, ) raise typer.Exit(1) @@ -753,9 +708,7 @@ def train( "decoder": stage2_decoder.value if stage2_decoder is not None else None, "k_max": stage2_k_max, "context_dim": stage2_context_dim, - "stage1_context": stage2_stage1_context.value - if stage2_stage1_context is not None - else None, + "stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, }.items() if v is not None } @@ -834,9 +787,7 @@ def train( ): stage_wgan = {**shared_wgan_overrides, **stage_specific} if stage_wgan: - overrides.setdefault(stage_name, {}).setdefault("wgan", {}).update( - stage_wgan - ) + overrides.setdefault(stage_name, {}).setdefault("wgan", {}).update(stage_wgan) cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) gconfig.validate_config(cfg) @@ -845,19 +796,13 @@ def train( _device = torch.device(device) if device else gconfig.auto_device() if batch_size_auto: - est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( - cfg, training=True, stage="stage1" - ) + est_hidden_dim, est_n_blocks = _batch_size_estimate_dims(cfg, training=True, stage="stage1") try: - t["batch_size"] = gconfig.estimate_batch_size( - est_hidden_dim, est_n_blocks, _device - ) + t["batch_size"] = gconfig.estimate_batch_size(est_hidden_dim, est_n_blocks, _device) except ValueError as exc: typer.echo(f"error: {exc}", err=True) raise typer.Exit(1) - typer.echo( - f"batch_size: {t['batch_size']} (auto-estimated from free GPU memory)" - ) + typer.echo(f"batch_size: {t['batch_size']} (auto-estimated from free GPU memory)") if out is not None: out_dir = out @@ -913,39 +858,19 @@ def new_run( n_blocks: Annotated[Optional[int], typer.Option("--n-blocks", "-n")] = None, emb_dim: Annotated[Optional[int], typer.Option("--emb-dim", "-E")] = None, dropout: Annotated[Optional[float], typer.Option("--dropout", "-d")] = None, - stage1_generator: Annotated[ - Optional[Mode], typer.Option("--stage1-generator") - ] = None, - stage1_hidden_dim: Annotated[ - Optional[int], typer.Option("--stage1-hidden-dim") - ] = None, - stage1_n_res_blocks: Annotated[ - Optional[int], typer.Option("--stage1-n-res-blocks") - ] = None, + stage1_generator: Annotated[Optional[Mode], typer.Option("--stage1-generator")] = None, + stage1_hidden_dim: Annotated[Optional[int], typer.Option("--stage1-hidden-dim")] = None, + stage1_n_res_blocks: Annotated[Optional[int], typer.Option("--stage1-n-res-blocks")] = None, stage1_dropout: Annotated[Optional[float], typer.Option("--stage1-dropout")] = None, - stage2_generator: Annotated[ - Optional[Mode], typer.Option("--stage2-generator") - ] = None, - stage2_hidden_dim: Annotated[ - Optional[int], typer.Option("--stage2-hidden-dim") - ] = None, - stage2_n_res_blocks: Annotated[ - Optional[int], typer.Option("--stage2-n-res-blocks") - ] = None, + stage2_generator: Annotated[Optional[Mode], typer.Option("--stage2-generator")] = None, + stage2_hidden_dim: Annotated[Optional[int], typer.Option("--stage2-hidden-dim")] = None, + stage2_n_res_blocks: Annotated[Optional[int], typer.Option("--stage2-n-res-blocks")] = None, stage2_dropout: Annotated[Optional[float], typer.Option("--stage2-dropout")] = None, - stage2_decoder: Annotated[ - Optional[Decoder], typer.Option("--stage2-decoder") - ] = None, + stage2_decoder: Annotated[Optional[Decoder], typer.Option("--stage2-decoder")] = None, stage2_k_max: Annotated[Optional[int], typer.Option("--stage2-k-max")] = None, - stage2_context_dim: Annotated[ - Optional[int], typer.Option("--stage2-context-dim") - ] = None, - stage2_stage1_context: Annotated[ - Optional[Stage1Context], typer.Option("--stage2-stage1-context") - ] = None, - conditioning: Annotated[ - Optional[Conditioning], typer.Option("--conditioning") - ] = None, + stage2_context_dim: Annotated[Optional[int], typer.Option("--stage2-context-dim")] = None, + stage2_stage1_context: Annotated[Optional[Stage1Context], typer.Option("--stage2-stage1-context")] = None, + conditioning: Annotated[Optional[Conditioning], typer.Option("--conditioning")] = None, router: Annotated[Optional[bool], typer.Option("--router/--no-router")] = None, router_type: Annotated[Optional[str], typer.Option("--router-type")] = None, n_experts: Annotated[Optional[int], typer.Option("--n-experts")] = None, @@ -956,16 +881,13 @@ def new_run( ] = None, comment: Annotated[ Optional[str], - typer.Option( - "--comment", help="Free-text note recorded in config.toml's meta section" - ), + typer.Option("--comment", help="Free-text note recorded in config.toml's meta section"), ] = None, data: Annotated[ Optional[Path], typer.Option( "--data", - help="Dataset path to fill in the printed next-step command " - "(not stored in the config)", + help="Dataset path to fill in the printed next-step command (not stored in the config)", ), ] = None, force: Annotated[ @@ -977,9 +899,7 @@ def new_run( ] = False, dry_run: Annotated[ bool, - typer.Option( - "--dry-run", help="Print the resolved config without writing anything" - ), + typer.Option("--dry-run", help="Print the resolved config without writing anything"), ] = False, ) -> None: """Scaffold a new training run: resolve hyperparams to a config.toml and lay out its run dir. @@ -1030,9 +950,7 @@ def new_run( "decoder": stage2_decoder.value if stage2_decoder is not None else None, "k_max": stage2_k_max, "context_dim": stage2_context_dim, - "stage1_context": stage2_stage1_context.value - if stage2_stage1_context is not None - else None, + "stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, }.items() if v is not None } @@ -1116,9 +1034,7 @@ def new_run( @app.command() def predict( - data: Annotated[ - Path, typer.Argument(help="Parquet file or directory of parquet files") - ], + data: Annotated[Path, typer.Argument(help="Parquet file or directory of parquet files")], checkpoint: Annotated[ Path, typer.Option( @@ -1151,8 +1067,7 @@ def predict( typer.Option( "--batch-size", "-b", - help="Inference batch size, or 'auto' to estimate from free GPU " - "memory (cuda devices only)", + help="Inference batch size, or 'auto' to estimate from free GPU memory (cuda devices only)", ), ] = "4096", steps: Annotated[ @@ -1221,8 +1136,7 @@ def predict( if "sec_phys" not in ckpt.get("normalizer", {}): typer.echo( - "error: checkpoint has no normalizer.sec_phys — retrain with the " - "current code", + "error: checkpoint has no normalizer.sec_phys — retrain with the current code", err=True, ) raise typer.Exit(1) @@ -1235,9 +1149,7 @@ def predict( stage2_k_max = _stage_cfg(model_cfg, "stage2").get("k_max", K_MAX) if batch_size_auto: - est_hidden_dim, est_n_blocks = _batch_size_estimate_dims( - model_cfg, training=False - ) + est_hidden_dim, est_n_blocks = _batch_size_estimate_dims(model_cfg, training=False) try: batch_size_value = gconfig.estimate_batch_size( est_hidden_dim, @@ -1248,9 +1160,7 @@ def predict( except ValueError as exc: typer.echo(f"error: {exc}", err=True) raise typer.Exit(1) - typer.echo( - f"batch_size: {batch_size_value} (auto-estimated from free GPU memory)" - ) + typer.echo(f"batch_size: {batch_size_value} (auto-estimated from free GPU memory)") assert batch_size_value is not None bs = batch_size_value @@ -1311,16 +1221,10 @@ def predict( return iter_file_chunks(path, offset=offset, k_max=stage2_k_max) return iter_cond_chunks(path, offset=offset) - cond_pdg_topn = ( - pdg_topn_map.class_map if particle_conditioning == "onehot" else None - ) - cond_mat_topn = ( - mat_topn_map.class_map if material_conditioning == "onehot" else None - ) + cond_pdg_topn = pdg_topn_map.class_map if particle_conditioning == "onehot" else None + cond_mat_topn = mat_topn_map.class_map if material_conditioning == "onehot" else None - def _concat( - a: dict[str, np.ndarray], b: dict[str, np.ndarray] - ) -> dict[str, np.ndarray]: + def _concat(a: dict[str, np.ndarray], b: dict[str, np.ndarray]) -> dict[str, np.ndarray]: return {k: np.concatenate([a[k], b[k]], axis=0) for k in a} def _process(piece: dict[str, np.ndarray]) -> None: @@ -1352,19 +1256,13 @@ def predict( cc = torch.from_numpy(cond_cont).float().to(_device) ck = torch.from_numpy(cond_cat).long().to(_device) - stage1_norm, n_sec_pred = sample_stage1( - model, cc, ck, steps=steps, ddpm_steps=stage1_ddpm_steps - ) + stage1_norm, n_sec_pred = sample_stage1(model, cc, ck, steps=steps, ddpm_steps=stage1_ddpm_steps) if coord == Coord.global_: # A fresh v0.3.0 Stage1Model owns no n_sec_head — # sample_stage1 returns n_sec_pred=None then, so ask stage 2. - n_sec_pred = resolve_n_sec( - model, sec_decoder, cc, ck, stage1_norm, n_sec_pred - ) - sec_cont, sec_type, _sec_valid_pred = sample_stage2( - sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps - ) + n_sec_pred = resolve_n_sec(model, sec_decoder, cc, ck, stage1_norm, n_sec_pred) + sec_cont, sec_type, _sec_valid_pred = sample_stage2(sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps) n_sec_pred_np = n_sec_pred.cpu().numpy() pred = stage1_norm.cpu().numpy() # normalised @@ -1387,14 +1285,8 @@ def predict( "material": piece["material"], "layer_id": piece["layer_id"], "n_sec": piece["n_sec"], - **{ - f"pred_{name}": raw[:, j] - for j, name in enumerate(LOCAL_TARGET_NAMES) - }, - **{ - f"true_{name}": target_raw[:, j] - for j, name in enumerate(LOCAL_TARGET_NAMES) - }, + **{f"pred_{name}": raw[:, j] for j, name in enumerate(LOCAL_TARGET_NAMES)}, + **{f"true_{name}": target_raw[:, j] for j, name in enumerate(LOCAL_TARGET_NAMES)}, } ) else: @@ -1404,9 +1296,7 @@ def predict( # (hence delta_e == edep + e_sec) holds by construction. e_sec_pred # doubles as the stick-breaking energy budget for the Stage-2 decode # below, since the model has no other source for it at inference. - edep, e_sec_pred, _post_E, delta_e = energy_simplex_decode( - raw[:, 1:3], piece["pre_E"] - ) + edep, e_sec_pred, _post_E, delta_e = energy_simplex_decode(raw[:, 1:3], piece["pre_E"]) # Normalise predicted direction then rotate back to world frame post_dir_local = raw[:, 3:6].copy() @@ -1419,43 +1309,31 @@ def predict( travel_dir_local = raw[:, 6:9].copy() norms = np.linalg.norm(travel_dir_local, axis=1, keepdims=True) travel_dir_local /= np.where(norms < 1e-8, 1.0, norms) - post_pos_world = reconstruct_post_pos( - piece["pre_pos"], piece["pre_dir"], step_length, travel_dir_local - ) + post_pos_world = reconstruct_post_pos(piece["pre_pos"], piece["pre_dir"], step_length, travel_dir_local) # particle_type.target="physical": sec_pdg_code is a reporting- # only nearest-known-PDG label (never fed back into the model — # "no snapping at inference"). "onehot"/"embedding": PDG # resolution IS the secondary's identity — see # decode_secondary_identity's docstring. - sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, _l1_dist = ( - decode_secondary_identity( - sec_decoder, - sec_cont, - sec_type, - n_sec_pred_np, - e_sec_pred, - piece["pre_dir"], - sec_phys_norm, - pdg_map, - pdg_topn_map, - other_policy, - None, - ) + sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, _l1_dist = decode_secondary_identity( + sec_decoder, + sec_cont, + sec_type, + n_sec_pred_np, + e_sec_pred, + piece["pre_dir"], + sec_phys_norm, + pdg_map, + pdg_topn_map, + other_policy, + None, ) - sec_pdg_list = [ - sec_pdg_code[i, :n].tolist() for i, n in enumerate(n_sec_pred_np) - ] + sec_pdg_list = [sec_pdg_code[i, :n].tolist() for i, n in enumerate(n_sec_pred_np)] sec_E_list = [sec_E[i, :n].tolist() for i, n in enumerate(n_sec_pred_np)] - sec_dx_list = [ - sec_dir_world[i, :n, 0].tolist() for i, n in enumerate(n_sec_pred_np) - ] - sec_dy_list = [ - sec_dir_world[i, :n, 1].tolist() for i, n in enumerate(n_sec_pred_np) - ] - sec_dz_list = [ - sec_dir_world[i, :n, 2].tolist() for i, n in enumerate(n_sec_pred_np) - ] + sec_dx_list = [sec_dir_world[i, :n, 0].tolist() for i, n in enumerate(n_sec_pred_np)] + sec_dy_list = [sec_dir_world[i, :n, 1].tolist() for i, n in enumerate(n_sec_pred_np)] + sec_dz_list = [sec_dir_world[i, :n, 2].tolist() for i, n in enumerate(n_sec_pred_np)] table = pa.table( { @@ -1537,9 +1415,7 @@ def predict( typer.echo(f"reference: {ref_path}") if skipped: - codes = ", ".join( - f"{pdg} ({count})" for pdg, count in sorted(unknown_pdg_counts.items()) - ) + codes = ", ".join(f"{pdg} ({count})" for pdg, count in sorted(unknown_pdg_counts.items())) typer.echo( f"warning: skipped {skipped:,} row(s) with unknown PDG code(s): {codes}", err=True, @@ -1587,9 +1463,7 @@ def _seed_from_data(files: list[Path], n_events: int | None) -> dict[str, np.nda @app.command() def rollout( - data: Annotated[ - Path, typer.Argument(help="Parquet file/dir to seed showers from (real events)") - ], + data: Annotated[Path, typer.Argument(help="Parquet file/dir to seed showers from (real events)")], checkpoint: Annotated[ Path, typer.Option("--checkpoint", "-c", help="Checkpoint .pt (best.pt/last.pt)"), @@ -1609,9 +1483,7 @@ def rollout( help="Stop a track when its energy drops below this [MeV]", ), ] = 0.1, - max_steps: Annotated[ - int, typer.Option("--max-steps", help="Max steps per individual track") - ] = 1000, + max_steps: Annotated[int, typer.Option("--max-steps", help="Max steps per individual track")] = 1000, steps: Annotated[ int, typer.Option( @@ -1629,9 +1501,7 @@ def rollout( "requires a checkpoint trained with EMA enabled.", ), ] = Weights.raw, - batch_size: Annotated[ - int, typer.Option("--batch-size", "-b", help="Tracks stepped per model forward") - ] = 4096, + batch_size: Annotated[int, typer.Option("--batch-size", "-b", help="Tracks stepped per model forward")] = 4096, max_tracks_per_event: Annotated[ Optional[int], typer.Option( @@ -1646,15 +1516,9 @@ def rollout( help="Override the oracle's NN-distance escape threshold [mm]", ), ] = None, - n_events: Annotated[ - Optional[int], typer.Option("--n-events", help="Cap number of seed events") - ] = None, - device: Annotated[ - Optional[str], typer.Option("--device", "-d", help="cpu | cuda | mps (auto)") - ] = None, - out: Annotated[ - Optional[Path], typer.Option("--out", "-o", help="Output steps parquet") - ] = None, + n_events: Annotated[Optional[int], typer.Option("--n-events", help="Cap number of seed events")] = None, + device: Annotated[Optional[str], typer.Option("--device", "-d", help="cpu | cuda | mps (auto)")] = None, + out: Annotated[Optional[Path], typer.Option("--out", "-o", help="Output steps parquet")] = None, seed: Annotated[ Optional[int], typer.Option("--seed", help="Torch/numpy seed for reproducibility"), @@ -1679,8 +1543,7 @@ def rollout( if "sec_phys" not in ckpt.get("normalizer", {}): typer.echo( - "error: checkpoint has no normalizer.sec_phys — retrain with the " - "current code", + "error: checkpoint has no normalizer.sec_phys — retrain with the current code", err=True, ) raise typer.Exit(1) @@ -1730,10 +1593,7 @@ def rollout( typer.echo(f"loaded checkpoint: {checkpoint} (weights: {weights.value})") oracle = GeometryOracle.load(geometry) - typer.echo( - f"loaded geometry oracle: {geometry} " - f"(escape_threshold={oracle.escape_threshold:.3f})" - ) + typer.echo(f"loaded geometry oracle: {geometry} (escape_threshold={oracle.escape_threshold:.3f})") files = find_parquet_files(data) seeds = _seed_from_data(files, n_events) @@ -1821,9 +1681,7 @@ def rollout( # stage2_model.particle_type.target="embedding"; omitted (not # written as null) otherwise, so giant.analysis can tell "not # applicable to this checkpoint" apart from "collector empty". - **( - {"type_embedding_l1_dist": l1_summary} if l1_summary is not None else {} - ), + **({"type_embedding_l1_dist": l1_summary} if l1_summary is not None else {}), # Full architecture spec baked into the checkpoint — includes the # entire router sub-dict, not just a hand-picked subset, so any # model knob (router type/n_experts, noise_dim, vocab sizes, ...) @@ -1855,9 +1713,7 @@ app.add_typer(analyze_app, name="analyze") def analyze_prep( rollout_yaml: Annotated[ Path, - typer.Argument( - help="giant rollout YAML sidecar (names the rollout + reference files)" - ), + typer.Argument(help="giant rollout YAML sidecar (names the rollout + reference files)"), ], run_dir: Annotated[ Optional[Path], @@ -1872,9 +1728,7 @@ def analyze_prep( top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6, chunks: Annotated[ int, - typer.Option( - "--chunks", help="Split each plot's data into this many event_id chunks" - ), + typer.Option("--chunks", help="Split each plot's data into this many event_id chunks"), ] = 1, ) -> None: """Read the rollout YAML → shared.json + run_meta.json in the run directory.""" @@ -1894,15 +1748,9 @@ def analyze_prep( @analyze_app.command("compute-one") def analyze_compute_one( - id: Annotated[ - str, typer.Option("--id", help="Catalog plot id (see `analyze list`)") - ], - run_dir: Annotated[ - Path, typer.Option("--run-dir", help="Run directory from `analyze prep`") - ], - chunk: Annotated[ - int, typer.Option("--chunk", help="Chunk index (see `analyze prep --chunks`)") - ] = 0, + id: Annotated[str, typer.Option("--id", help="Catalog plot id (see `analyze list`)")], + run_dir: Annotated[Path, typer.Option("--run-dir", help="Run directory from `analyze prep`")], + chunk: Annotated[int, typer.Option("--chunk", help="Chunk index (see `analyze prep --chunks`)")] = 0, ) -> None: """Run one (plot, chunk)'s streaming reduction (this is what each condor job runs).""" from giant.analysis import compute_one @@ -1913,12 +1761,8 @@ def analyze_compute_one( @analyze_app.command("merge-one") def analyze_merge_one( - id: Annotated[ - str, typer.Option("--id", help="Catalog plot id (see `analyze list`)") - ], - run_dir: Annotated[ - Path, typer.Option("--run-dir", help="Run directory from `analyze prep`") - ], + id: Annotated[str, typer.Option("--id", help="Catalog plot id (see `analyze list`)")], + run_dir: Annotated[Path, typer.Option("--run-dir", help="Run directory from `analyze prep`")], ) -> None: """Merge one plot's chunk partials into its final reduced JSON. @@ -1942,14 +1786,10 @@ def analyze_list() -> None: @analyze_app.command("render") def analyze_render( - run_dir: Annotated[ - Path, typer.Argument(help="Run directory from `analyze prep` (holds reduced/)") - ], + run_dir: Annotated[Path, typer.Argument(help="Run directory from `analyze prep` (holds reduced/)")], gallery: Annotated[ bool, - typer.Option( - "--gallery/--no-gallery", help="Run `gallery generate` after rendering" - ), + typer.Option("--gallery/--no-gallery", help="Run `gallery generate` after rendering"), ] = False, ) -> None: """Render reduced artifacts to styled PDFs + gallery metadata (local; needs LaTeX).""" @@ -1971,9 +1811,7 @@ def analyze_submit( help="Override the run directory (default: /analysis_runs/analysis_)", ), ] = None, - docker_image: Annotated[ - str, typer.Option("--docker-image") - ] = "cverstege/alma9-gridjob", + docker_image: Annotated[str, typer.Option("--docker-image")] = "cverstege/alma9-gridjob", request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 8192, remote: Annotated[ bool, @@ -1989,9 +1827,7 @@ def analyze_submit( n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4, n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50, top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6, - dry_run: Annotated[ - bool, typer.Option("--dry-run", help="Write files but don't condor_submit") - ] = False, + dry_run: Annotated[bool, typer.Option("--dry-run", help="Write files but don't condor_submit")] = False, ) -> None: """prep + write the HTCondor submit description (one job per plot x chunk), then submit.""" import subprocess diff --git a/giant/config.py b/giant/config.py index c779969..92c42d9 100644 --- a/giant/config.py +++ b/giant/config.py @@ -299,13 +299,7 @@ DEFAULT_CONFIG: dict = { def git_hash() -> str: try: - return ( - subprocess.check_output( - ["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL - ) - .decode() - .strip() - ) + return subprocess.check_output(["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL).decode().strip() except Exception: return "unknown" @@ -357,12 +351,8 @@ def estimate_batch_size( calibration since there's no backward graph or optimizer state. """ if device.type != "cuda": - raise ValueError( - f"--batch-size auto is only supported on cuda devices, got {device.type!r}" - ) - device_index = ( - device.index if device.index is not None else torch.cuda.current_device() - ) + raise ValueError(f"--batch-size auto is only supported on cuda devices, got {device.type!r}") + device_index = device.index if device.index is not None else torch.cuda.current_device() free_bytes, _total_bytes = torch.cuda.mem_get_info(device_index) if training: ref_bytes, ref_batch_size, ref_hidden_dim, ref_n_blocks = ( @@ -690,11 +680,7 @@ def validate_config(cfg: dict) -> None: for stage_name in ("stage1_model", "stage2_model"): router = _get_path(cfg, f"{stage_name}.router") or {} - if ( - router.get("enabled") - and router.get("type") in ("pdg", "process") - and particle_type == "physical" - ): + if router.get("enabled") and router.get("type") in ("pdg", "process") and particle_type == "physical": raise ValueError( f"{stage_name}.router.type = {router['type']!r} builds its " "own training-vocab-scoped embedding, incompatible with " @@ -704,9 +690,7 @@ def validate_config(cfg: dict) -> None: "conditioning.particle.type" ) - if _get_path(cfg, "stage2_model.router.tie_to_stage1") and not _get_path( - cfg, "stage1_model.active" - ): + if _get_path(cfg, "stage2_model.router.tie_to_stage1") and not _get_path(cfg, "stage1_model.active"): raise ValueError( "stage2_model.router.tie_to_stage1 = true requires " "stage1_model.active = true (there is no stage-1 router to tie to)" @@ -737,10 +721,7 @@ def validate_config(cfg: dict) -> None: if _get_path(cfg, "stage2_model.decoder") == "autoregressive": history = _get_path(cfg, "stage2_model.autoregressive.history") if history not in ("markov", "attention"): - raise ValueError( - f"stage2_model.autoregressive.history = {history!r} — must " - "be 'markov' or 'attention'" - ) + raise ValueError(f"stage2_model.autoregressive.history = {history!r} — must be 'markov' or 'attention'") teacher_forcing = _get_path(cfg, "stage2_model.autoregressive.teacher_forcing") if teacher_forcing not in ("always", "scheduled", "never"): raise ValueError( diff --git a/giant/data/dataset.py b/giant/data/dataset.py index 13c78fc..6e2e282 100644 --- a/giant/data/dataset.py +++ b/giant/data/dataset.py @@ -123,9 +123,7 @@ class StreamingStepsDataset(IterableDataset): buf_n = 0 for path in files: - for chunk in iter_file_chunks( - path, offset=self._offsets[path], k_max=self.k_max - ): + for chunk in iter_file_chunks(path, offset=self._offsets[path], k_max=self.k_max): mask = sorted_membership(chunk["event_id"], self._events_arr) if not mask.any(): continue diff --git a/giant/data/loader.py b/giant/data/loader.py index 43a1a44..94acfba 100644 --- a/giant/data/loader.py +++ b/giant/data/loader.py @@ -115,9 +115,7 @@ def _pad_dir_col(dx: pd.Series, dy: pd.Series, dz: pd.Series, K: int) -> np.ndar return out -def _df_to_dict( - df: pd.DataFrame, offset: int = 0, k_max: int = K_MAX -) -> dict[str, np.ndarray]: +def _df_to_dict(df: pd.DataFrame, offset: int = 0, k_max: int = K_MAX) -> dict[str, np.ndarray]: has_sec_lists = "sec_E_list" in df.columns d: dict[str, np.ndarray] = { @@ -136,9 +134,7 @@ def _df_to_dict( # / ProcessRouter). Guarded like has_sec_lists: older parquet # conversions predating this column still load fine. "process": ( - df["process"].to_numpy(dtype=object) - if "process" in df.columns - else np.full(len(df), "", dtype=object) + df["process"].to_numpy(dtype=object) if "process" in df.columns else np.full(len(df), "", dtype=object) ), "step_length": df["step_length"].to_numpy(dtype=np.float32), "post_E": df["post_E"].to_numpy(dtype=np.float32), @@ -151,16 +147,12 @@ def _df_to_dict( if has_sec_lists: d["sec_E_list"] = _pad_list_col(df["sec_E_list"], k_max) d["sec_pdg_list"] = _pad_list_col_int(df["sec_pdg_list"], k_max) - d["sec_dir_list"] = _pad_dir_col( - df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], k_max - ) + d["sec_dir_list"] = _pad_dir_col(df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], k_max) return d -def load_steps( - path: str | Path, offset: int = 0, k_max: int = K_MAX -) -> dict[str, np.ndarray]: +def load_steps(path: str | Path, offset: int = 0, k_max: int = K_MAX) -> dict[str, np.ndarray]: return _df_to_dict(pd.read_parquet(path), offset=offset, k_max=k_max) @@ -170,9 +162,7 @@ def load_event_ids(path: str | Path, offset: int = 0) -> np.ndarray: return _offset_event_id(ids, offset) -def iter_file_chunks( - path: str | Path, offset: int = 0, k_max: int = K_MAX -) -> Iterator[dict[str, np.ndarray]]: +def iter_file_chunks(path: str | Path, offset: int = 0, k_max: int = K_MAX) -> Iterator[dict[str, np.ndarray]]: """Yield one parquet row-group at a time so a large file never fully loads. `k_max` sets the padded width of the sec_*_list columns (should match @@ -214,15 +204,11 @@ def _cond_df_to_dict(df: pd.DataFrame, offset: int = 0) -> dict[str, np.ndarray] } -def iter_cond_chunks( - path: str | Path, offset: int = 0 -) -> Iterator[dict[str, np.ndarray]]: +def iter_cond_chunks(path: str | Path, offset: int = 0) -> Iterator[dict[str, np.ndarray]]: """Yield conditioning-only row-groups (no post-step columns read from disk).""" pf = pq.ParquetFile(path) for i in range(pf.num_row_groups): - yield _cond_df_to_dict( - pf.read_row_group(i, columns=_COND_COLS).to_pandas(), offset=offset - ) + yield _cond_df_to_dict(pf.read_row_group(i, columns=_COND_COLS).to_pandas(), offset=offset) def build_index_maps( @@ -315,9 +301,7 @@ class TopNMap: other_members: dict -def build_topn_map_from_files( - files: list[Path], column: str, n_classes: int, cast=str -) -> TopNMap: +def build_topn_map_from_files(files: list[Path], column: str, n_classes: int, cast=str) -> TopNMap: """Scan `column` and build a frequency-capped value->index map, structurally identical to `build_process_map_from_files` (shares its ranking core via `_topn_plus_other_map`), generalized over the source column and key type. diff --git a/giant/data/setup_cache.py b/giant/data/setup_cache.py index a0a3981..b1a613e 100644 --- a/giant/data/setup_cache.py +++ b/giant/data/setup_cache.py @@ -108,10 +108,7 @@ def normalizer_key( # affect which cond_cont columns are computed for real vs. zero-filled # (giant.data.transforms._physical_cond_columns), so both must be part of # the key or two mixed-axis runs could collide on the same cache entry. - return ( - f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}" - f"_mcond={material_conditioning}" - ) + return f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}_mcond={material_conditioning}" # Top-N-map axes: "pdg" keys match pdg_map's int @@ -126,10 +123,7 @@ def topn_key(axis: str, n_classes: int) -> str: sidecar stays reusable across runs with different emb_dim (see the dict[int, dict] precedent `proc_maps` sets, keyed by n_experts).""" if axis not in _TOPN_AXIS_CASTS: - raise ValueError( - f"unknown top-N map axis {axis!r}, expected one of " - f"{sorted(_TOPN_AXIS_CASTS)}" - ) + raise ValueError(f"unknown top-N map axis {axis!r}, expected one of {sorted(_TOPN_AXIS_CASTS)}") return f"{axis}:{n_classes}" @@ -165,9 +159,7 @@ class NormalizerEntry: "tgt_norm": self.tgt_norm.to_dict(), "sec_phys_norm": self.sec_phys_norm.to_dict(), "n_train_steps": self.n_train_steps, - "energy_quantiles": np.asarray( - self.energy_quantiles, dtype=np.float32 - ).tolist(), + "energy_quantiles": np.asarray(self.energy_quantiles, dtype=np.float32).tolist(), } @classmethod @@ -234,13 +226,8 @@ class SetupCache: np.array(d["event_index"]["counts"], dtype=np.int64), ) proc_maps = {int(k): v for k, v in d.get("proc_maps", {}).items()} - normalizers = { - k: NormalizerEntry.from_json(v) for k, v in d.get("normalizers", {}).items() - } - topn_maps = { - k: topnmap_from_json(v, axis=k.split(":", 1)[0]) - for k, v in d.get("topn_maps", {}).items() - } + normalizers = {k: NormalizerEntry.from_json(v) for k, v in d.get("normalizers", {}).items()} + topn_maps = {k: topnmap_from_json(v, axis=k.split(":", 1)[0]) for k, v in d.get("topn_maps", {}).items()} return cls( fingerprint=d["fingerprint"], git_hash=d.get("git_hash", "unknown"), @@ -263,18 +250,14 @@ class SetupCache: fingerprint=other.fingerprint, git_hash=other.git_hash, vocab=other.vocab if other.vocab is not None else self.vocab, - event_index=( - other.event_index if other.event_index is not None else self.event_index - ), + event_index=(other.event_index if other.event_index is not None else self.event_index), proc_maps={**self.proc_maps, **other.proc_maps}, normalizers={**self.normalizers, **other.normalizers}, topn_maps={**self.topn_maps, **other.topn_maps}, ) -def load( - data: str | Path, files: list[Path], echo=lambda *a, **k: None -) -> SetupCache | None: +def load(data: str | Path, files: list[Path], echo=lambda *a, **k: None) -> SetupCache | None: """Load and validate the sidecar for `data`; `None` on any miss (never raises). A missing file, corrupt JSON, format-version mismatch, dimension-constant @@ -298,9 +281,7 @@ def load( echo("setup cache: format version changed — ignoring stale cache") return None if raw.get("dims") != _DIMS: - echo( - "setup cache: model dimension constants changed — ignoring stale cache" - ) + echo("setup cache: model dimension constants changed — ignoring stale cache") return None fp = fingerprint_files(files) if raw.get("fingerprint") != fp: @@ -343,9 +324,7 @@ def save( with open(lock_path, "a") as lock_file: fcntl.flock(lock_file, fcntl.LOCK_EX) try: - base = load(data, files, echo=lambda *a, **k: None) or SetupCache.empty( - files - ) + base = load(data, files, echo=lambda *a, **k: None) or SetupCache.empty(files) merged = base.merge(sections) payload = json.dumps(merged.to_json(), separators=(",", ":")) tmp.write_text(payload) @@ -353,9 +332,7 @@ def save( finally: fcntl.flock(lock_file, fcntl.LOCK_UN) except OSError as exc: - echo( - f"setup cache: could not write {path} ({exc}) — continuing without caching" - ) + echo(f"setup cache: could not write {path} ({exc}) — continuing without caching") try: tmp.unlink(missing_ok=True) except OSError: @@ -366,16 +343,12 @@ def compute_event_index_from_files(files: list[Path]) -> tuple[np.ndarray, np.nd """Unique event ids + per-event row (step) counts, across all `files`.""" if not files: return np.empty(0, dtype=np.int64), np.empty(0, dtype=np.int64) - all_ids = np.concatenate( - [load_event_ids(f, offset=event_id_offset(i)) for i, f in enumerate(files)] - ) + all_ids = np.concatenate([load_event_ids(f, offset=event_id_offset(i)) for i, f in enumerate(files)]) unique_ids, counts = np.unique(all_ids, return_counts=True) return unique_ids, counts -def n_train_steps_for_split( - unique_ids: np.ndarray, counts: np.ndarray, train_events_arr: np.ndarray -) -> int: +def n_train_steps_for_split(unique_ids: np.ndarray, counts: np.ndarray, train_events_arr: np.ndarray) -> int: """Row (step) count summed over whichever `unique_ids` fall in `train_events_arr`. `train_events_arr` must be ascending and duplicate-free (as produced by diff --git a/giant/data/transforms.py b/giant/data/transforms.py index bfe7234..4dfc672 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -84,9 +84,7 @@ def energy_simplex_encode( return z.astype(np.float32) -def energy_simplex_decode( - z: np.ndarray, pre_E: np.ndarray -) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: +def energy_simplex_decode(z: np.ndarray, pre_E: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Inverse of `energy_simplex_encode`: ALR coords + pre_E → physical energies. A softmax over `[z_edep, z_sec, 0]` recovers the three simplex fractions, so @@ -118,9 +116,7 @@ def _rodrigues_axis(pre_dir: np.ndarray) -> np.ndarray: arbitrary second operand) on every row; profiling on a 114M-row file showed `np.cross` as the single hottest call inside this rotation. """ - axis = np.stack( - [pre_dir[:, 1], -pre_dir[:, 0], np.zeros_like(pre_dir[:, 0])], axis=1 - ) + axis = np.stack([pre_dir[:, 1], -pre_dir[:, 0], np.zeros_like(pre_dir[:, 0])], axis=1) axis_norm = np.linalg.norm(axis, axis=1, keepdims=True) # (N,1) # axis_norm ~ 0 happens at BOTH poles: pre_dir ~ +ẑ (forward) and # pre_dir ~ -ẑ (near-exact backscatter) — ‖pre_dir × ẑ‖ = sin(angle to @@ -199,9 +195,7 @@ def local_frame_rotation(pre_dir: np.ndarray, post_dir: np.ndarray) -> np.ndarra kxv = _cross_with_z_axis(axis, post_dir) # (N,3) kdv = (axis * post_dir).sum(axis=1, keepdims=True) # (N,1) - return (post_dir * cos_t + kxv * sin_t + axis * kdv * (1.0 - cos_t)).astype( - np.float32 - ) + return (post_dir * cos_t + kxv * sin_t + axis * kdv * (1.0 - cos_t)).astype(np.float32) class Normalizer: @@ -341,9 +335,7 @@ def sorted_membership(values: np.ndarray, sorted_arr: np.ndarray) -> np.ndarray: return sorted_arr[idx] == values -def _vectorized_map_lookup( - values: np.ndarray, mapping: dict, strict: bool = True, default: int = 0 -) -> np.ndarray: +def _vectorized_map_lookup(values: np.ndarray, mapping: dict, strict: bool = True, default: int = 0) -> np.ndarray: """Vectorized equivalent of `np.array([mapping[v] for v in values], dtype=np.int64)`. Replaces a per-element Python dict lookup with one `searchsorted` call. @@ -387,9 +379,7 @@ def travel_direction(pre_pos: np.ndarray, post_pos: np.ndarray) -> np.ndarray: disp = post_pos - pre_pos norm = np.linalg.norm(disp, axis=1, keepdims=True) safe_norm = np.where(norm < 1e-7, 1.0, norm) - return np.where(norm < 1e-7, np.array([[0.0, 0.0, 1.0]]), disp / safe_norm).astype( - np.float32 - ) + return np.where(norm < 1e-7, np.array([[0.0, 0.0, 1.0]]), disp / safe_norm).astype(np.float32) def reconstruct_post_pos( @@ -408,9 +398,7 @@ def reconstruct_post_pos( return (pre_pos + step_length.reshape(-1, 1) * travel_dir_world).astype(np.float32) -def inv_local_frame_rotation( - pre_dir: np.ndarray, post_dir_local: np.ndarray -) -> np.ndarray: +def inv_local_frame_rotation(pre_dir: np.ndarray, post_dir_local: np.ndarray) -> np.ndarray: """Inverse of local_frame_rotation: rotate from local frame back to world frame. Applies R^T (same axis, negative angle) to post_dir_local. @@ -424,9 +412,7 @@ def inv_local_frame_rotation( kdv = (axis * post_dir_local).sum(axis=1, keepdims=True) # Negative angle: sin_t → -sin_t - return (post_dir_local * cos_t - kxv * sin_t + axis * kdv * (1.0 - cos_t)).astype( - np.float32 - ) + return (post_dir_local * cos_t - kxv * sin_t + axis * kdv * (1.0 - cos_t)).astype(np.float32) _STICK_LOGIT_CLIP = 10.0 # logit value used for the last valid secondary slot @@ -491,14 +477,10 @@ def encode_secondaries( remaining_raw = e_sec - cumsum[:, i - 1] shortfall_flagged |= sec_valid[:, i] & (remaining_raw < -_SHORTFALL_TOL) remaining = np.maximum(remaining_raw, _EPS) - f = np.clip( - sec_E_list[:, i].astype(np.float64) / remaining, _EPS, 1.0 - _EPS - ) + f = np.clip(sec_E_list[:, i].astype(np.float64) / remaining, _EPS, 1.0 - _EPS) logit = np.log(f / (1.0 - f)).astype(np.float32) # Last valid slot: give it the full remaining budget - is_last = sec_valid[:, i] & ~( - sec_valid[:, i + 1] if i + 1 < K else np.zeros(N, dtype=bool) - ) + is_last = sec_valid[:, i] & ~(sec_valid[:, i + 1] if i + 1 < K else np.zeros(N, dtype=bool)) logit = np.where(is_last, _STICK_LOGIT_CLIP, logit) logit = np.where( sec_valid[:, i], @@ -525,9 +507,7 @@ def encode_secondaries( # Only rotate valid slots; leave padded slots as (0,0,1) or whatever. valid_mask = sec_valid[:, i] if valid_mask.any(): - dir_local[valid_mask, i] = local_frame_rotation( - pre_dir[valid_mask], sec_dir_list[valid_mask, i] - ) + dir_local[valid_mask, i] = local_frame_rotation(pre_dir[valid_mask], sec_dir_list[valid_mask, i]) if sec_pdg_list is not None: from giant.particles import particle_phys_array @@ -553,9 +533,7 @@ def encode_secondaries( return sec_cont.astype(np.float32) -def encode_secondary_type_idx( - sec_pdg_list: np.ndarray, sec_valid: np.ndarray, class_map: dict -) -> np.ndarray: +def encode_secondary_type_idx(sec_pdg_list: np.ndarray, sec_valid: np.ndarray, class_map: dict) -> np.ndarray: """Per-secondary-slot class index into `class_map` — (N, K_MAX) int64. `class_map` is either a top-N-plus-other map's `class_map` @@ -583,9 +561,7 @@ def encode_secondary_type_idx( # isn't guaranteed to be a key — an arbitrary present one always is). dummy = next(iter(class_map)) safe_pdg = np.where(sec_valid, sec_pdg_list, dummy) - idx = _vectorized_map_lookup(safe_pdg.reshape(-1), class_map, strict=True).reshape( - N, K - ) + idx = _vectorized_map_lookup(safe_pdg.reshape(-1), class_map, strict=True).reshape(N, K) return np.where(sec_valid, idx, 0).astype(np.int64) @@ -659,9 +635,7 @@ def decode_secondary_cont( for i in range(K): valid = sec_valid[:, i] if valid.any(): - sec_dir_world[valid, i] = inv_local_frame_rotation( - pre_dir[valid], dir_local[valid, i] - ) + sec_dir_world[valid, i] = inv_local_frame_rotation(pre_dir[valid], dir_local[valid, i]) return sec_E, sec_dir_world, sec_valid @@ -704,9 +678,7 @@ def decode_secondaries( sec_cont = sec_cont.copy() sec_cont[:, :, 4:6] = phys.reshape(N_, K_, 2) - sec_E, sec_dir_world, sec_valid = decode_secondary_cont( - sec_cont, n_sec, e_sec, pre_dir - ) + sec_E, sec_dir_world, sec_valid = decode_secondary_cont(sec_cont, n_sec, e_sec, pre_dir) log_mass = sec_cont[:, :, 4] # (N, K) charge = sec_cont[:, :, 5] # (N, K) @@ -760,16 +732,12 @@ def _physical_cond_columns( elif particle_conditioning in ("embedding", "onehot"): particle_cols = np.zeros((n, PARTICLE_PHYS_DIM), dtype=np.float32) else: - raise ValueError( - f"unknown conditioning.particle.type {particle_conditioning!r}" - ) + raise ValueError(f"unknown conditioning.particle.type {particle_conditioning!r}") if material_conditioning == "physical": from giant.materials import material_properties_array - z_eff, a_eff, density, x0, lambda_int = material_properties_array( - data["material"] - ).T + z_eff, a_eff, density, x0, lambda_int = material_properties_array(data["material"]).T material_cols = np.column_stack( [ z_eff, @@ -782,9 +750,7 @@ def _physical_cond_columns( elif material_conditioning in ("embedding", "onehot"): material_cols = np.zeros((n, MATERIAL_PHYS_DIM), dtype=np.float32) else: - raise ValueError( - f"unknown conditioning.material.type {material_conditioning!r}" - ) + raise ValueError(f"unknown conditioning.material.type {material_conditioning!r}") return np.column_stack([particle_cols, material_cols]).astype(np.float32) @@ -848,9 +814,7 @@ def build_cond_features( cond_cat = np.column_stack(cat_cols) if cond_normalizer is not None: - cond_cont = _cond_normalizer_transform( - cond_cont, cond_normalizer, particle_conditioning, material_conditioning - ) + cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, particle_conditioning, material_conditioning) return cond_cont, cond_cat @@ -972,13 +936,9 @@ def build_features( """ post_dir_local = local_frame_rotation(data["pre_dir"], data["post_dir"]) - travel_dir_local = local_frame_rotation( - data["pre_dir"], travel_direction(data["pre_pos"], data["post_pos"]) - ) + travel_dir_local = local_frame_rotation(data["pre_dir"], travel_direction(data["pre_pos"], data["post_pos"])) - energy_z = energy_simplex_encode( - data["edep"], data["e_sec"], data["post_E"], data["pre_E"] - ) # (N, 2) + energy_z = energy_simplex_encode(data["edep"], data["e_sec"], data["post_E"], data["pre_E"]) # (N, 2) target_s1 = np.column_stack( [ @@ -1014,9 +974,7 @@ def build_features( cat_cols.append(_vectorized_map_lookup(data["material"], mat_topn_map)) cond_cat = np.column_stack(cat_cols) # (N, 2/3/4) - n_sec_raw = data["n_sec"].astype( - np.int64 - ) # (N,) unclamped, for the valid-slot mask + n_sec_raw = data["n_sec"].astype(np.int64) # (N,) unclamped, for the valid-slot mask # Secondary continuous targets sec_E_list = data.get("sec_E_list") diff --git a/giant/geometry.py b/giant/geometry.py index ffe27da..3d137e8 100644 --- a/giant/geometry.py +++ b/giant/geometry.py @@ -31,10 +31,7 @@ import numpy as np import pandas as pd import pyarrow.parquet as pq -_INSTALL_HINT = ( - "the geometry oracle needs scikit-learn — install it with " - "`uv sync --extra cpu --extra geometry`" -) +_INSTALL_HINT = "the geometry oracle needs scikit-learn — install it with `uv sync --extra cpu --extra geometry`" def _require_sklearn(): @@ -63,9 +60,7 @@ class _SlabLookup: layer_ids: np.ndarray # (n_segments,) int64, layer_id of each segment radius_max: float # largest transverse radius seen in training data - def query( - self, pos: np.ndarray, margin: float - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + def query(self, pos: np.ndarray, margin: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]: other = [i for i in range(3) if i != self.axis] z = pos[:, self.axis] radius = np.sqrt(pos[:, other[0]] ** 2 + pos[:, other[1]] ** 2) @@ -75,11 +70,7 @@ class _SlabLookup: material = self.materials[idx] layer_id = self.layer_ids[idx] - escaped = ( - (z < self.z_edges[0] - margin) - | (z > self.z_edges[-1] + margin) - | (radius > self.radius_max + margin) - ) + escaped = (z < self.z_edges[0] - margin) | (z > self.z_edges[-1] + margin) | (radius > self.radius_max + margin) return material, layer_id, escaped @@ -300,16 +291,12 @@ def _fit_slab_lookup( """ other = [i for i in range(3) if i != axis] z = pos[:, axis].astype(np.float64) - radius = np.sqrt( - pos[:, other[0]].astype(np.float64) ** 2 - + pos[:, other[1]].astype(np.float64) ** 2 - ) + radius = np.sqrt(pos[:, other[0]].astype(np.float64) ** 2 + pos[:, other[1]].astype(np.float64) ** 2) z_min, z_max = float(z.min()), float(z.max()) if z_min == z_max: raise ValueError( - "all points share the same depth-axis coordinate — pick a " - "different `depth_axis` or use method='knn'/'svm'" + "all points share the same depth-axis coordinate — pick a different `depth_axis` or use method='knn'/'svm'" ) edges = np.linspace(z_min, z_max, n_bins + 1) bin_idx = np.clip(np.searchsorted(edges, z, side="right") - 1, 0, n_bins - 1) @@ -344,12 +331,7 @@ def _fit_slab_lookup( bin_layer = bin_layer[fill_from] # Run-length-encode consecutive bins sharing a label into segments. - changed = ( - np.flatnonzero( - (bin_material[1:] != bin_material[:-1]) | (bin_layer[1:] != bin_layer[:-1]) - ) - + 1 - ) + changed = np.flatnonzero((bin_material[1:] != bin_material[:-1]) | (bin_layer[1:] != bin_layer[:-1])) + 1 seg_starts = np.concatenate([[0], changed]) z_edges = np.concatenate([edges[seg_starts], edges[-1:]]) materials = bin_material[seg_starts] @@ -460,11 +442,7 @@ def build_geometry_oracle( # Escape threshold from the reference point spacing. Sample a subset for the # median 2-NN distance (the 1st neighbour of a training point is itself). nn = NearestNeighbors(n_neighbors=2).fit(X) - probe = ( - X - if len(X) <= 20_000 - else X[np.random.default_rng(seed).choice(len(X), 20_000, replace=False)] - ) + probe = X if len(X) <= 20_000 else X[np.random.default_rng(seed).choice(len(X), 20_000, replace=False)] d2, _ = nn.kneighbors(probe, n_neighbors=2) median_nn = float(np.median(d2[:, 1])) escape_threshold = escape_factor * median_nn diff --git a/giant/materials.py b/giant/materials.py index a15c8d2..1aed5de 100644 --- a/giant/materials.py +++ b/giant/materials.py @@ -64,18 +64,10 @@ MATERIAL_PROPERTIES: dict[str, MaterialProperties] = { "G4_CESIUM_IODIDE": MaterialProperties( z_eff=54.0, a_eff=129.904539, density=4.51, x0=1.860288, lambda_int=39.305990 ), - "G4_Pb": MaterialProperties( - z_eff=82.0, a_eff=207.216962, density=11.35, x0=0.561253, lambda_int=18.247950 - ), - "G4_W": MaterialProperties( - z_eff=74.0, a_eff=183.841648, density=19.30, x0=0.350418, lambda_int=10.311580 - ), - "G4_Cu": MaterialProperties( - z_eff=29.0, a_eff=63.545648, density=8.96, x0=1.435578, lambda_int=15.587940 - ), - "G4_Fe": MaterialProperties( - z_eff=26.0, a_eff=55.845113, density=7.874, x0=1.757493, lambda_int=16.990300 - ), + "G4_Pb": MaterialProperties(z_eff=82.0, a_eff=207.216962, density=11.35, x0=0.561253, lambda_int=18.247950), + "G4_W": MaterialProperties(z_eff=74.0, a_eff=183.841648, density=19.30, x0=0.350418, lambda_int=10.311580), + "G4_Cu": MaterialProperties(z_eff=29.0, a_eff=63.545648, density=8.96, x0=1.435578, lambda_int=15.587940), + "G4_Fe": MaterialProperties(z_eff=26.0, a_eff=55.845113, density=7.874, x0=1.757493, lambda_int=16.990300), "G4_BRASS": MaterialProperties( z_eff=30.939130, a_eff=68.500857, @@ -83,9 +75,7 @@ MATERIAL_PROPERTIES: dict[str, MaterialProperties] = { x0=1.367465, lambda_int=16.947420, ), - "G4_POLYSTYRENE": MaterialProperties( - z_eff=3.5, a_eff=6.509339, density=1.06, x0=41.312510, lambda_int=68.749880 - ), + "G4_POLYSTYRENE": MaterialProperties(z_eff=3.5, a_eff=6.509339, density=1.06, x0=41.312510, lambda_int=68.749880), "G4_PLASTIC_SC_VINYLTOLUENE": MaterialProperties( z_eff=3.368421, a_eff=6.219791, @@ -108,20 +98,15 @@ MATERIAL_PROPERTIES: dict[str, MaterialProperties] = { x0=30392.070000, lambda_int=71009.500000, ), - "G4_lAr": MaterialProperties( - z_eff=18.0, a_eff=39.947692, density=1.396, x0=14.003440, lambda_int=85.706400 - ), + "G4_lAr": MaterialProperties(z_eff=18.0, a_eff=39.947692, density=1.396, x0=14.003440, lambda_int=85.706400), } -def get_material_properties( - name: str, table: dict[str, MaterialProperties] | None = None -) -> MaterialProperties: +def get_material_properties(name: str, table: dict[str, MaterialProperties] | None = None) -> MaterialProperties: t = MATERIAL_PROPERTIES if table is None else table if name not in t: raise UnknownMaterialError( - f"material {name!r} is not in giant.materials.MATERIAL_PROPERTIES " - f"-- add it (known: {sorted(t)})" + f"material {name!r} is not in giant.materials.MATERIAL_PROPERTIES -- add it (known: {sorted(t)})" ) props = t[name] if any(v is None for v in props): @@ -134,9 +119,7 @@ def get_material_properties( return props -def material_properties_array( - names: np.ndarray, table: dict[str, MaterialProperties] | None = None -) -> np.ndarray: +def material_properties_array(names: np.ndarray, table: dict[str, MaterialProperties] | None = None) -> np.ndarray: """(N,) str material names -> (N, 5) float32 [z_eff, a_eff, density, x0, lambda_int].""" out = np.array( [get_material_properties(str(m), table) for m in np.asarray(names)], diff --git a/giant/model/network.py b/giant/model/network.py index 8aeadfb..bed790c 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -30,11 +30,7 @@ class SinusoidalEmbedding(nn.Module): super().__init__() assert dim % 2 == 0, "dim must be even" half = dim // 2 - freqs = torch.exp( - -math.log(10000) - * torch.arange(half, dtype=torch.float32) - / max(half - 1, 1) - ) + freqs = torch.exp(-math.log(10000) * torch.arange(half, dtype=torch.float32) / max(half - 1, 1)) self.register_buffer("freqs", freqs) def forward(self, t: torch.Tensor) -> torch.Tensor: @@ -43,9 +39,7 @@ class SinusoidalEmbedding(nn.Module): return torch.cat([args.sin(), args.cos()], dim=-1) # (B, dim) -def cat_col_layout( - particle_type: str, material_type: str -) -> tuple[int | None, int | None]: +def cat_col_layout(particle_type: str, material_type: str) -> tuple[int | None, int | None]: """`cond_cat` column indices for each axis's top-N-onehot index, or `None` if that axis isn't `"onehot"`. @@ -119,18 +113,14 @@ class ConditionEncoder(nn.Module): super().__init__() self.particle_cfg = dict(particle_cfg) self.material_cfg = dict(material_cfg) - self._particle_topn_col, self._material_topn_col = cat_col_layout( - particle_cfg["type"], material_cfg["type"] - ) + self._particle_topn_col, self._material_topn_col = cat_col_layout(particle_cfg["type"], material_cfg["type"]) p_type = particle_cfg["type"] p_emb_dim = particle_cfg["emb_dim"] if p_type == "embedding": self.pdg_emb = nn.Embedding(pdg_vocab, p_emb_dim) elif p_type == "physical": - self.particle_mlp = _make_axis_mlp( - PARTICLE_PHYS_DIM, p_emb_dim, particle_cfg.get("n_layers", 1) - ) + self.particle_mlp = _make_axis_mlp(PARTICLE_PHYS_DIM, p_emb_dim, particle_cfg.get("n_layers", 1)) elif p_type != "onehot": raise ValueError(f"unknown conditioning.particle.type {p_type!r}") @@ -139,9 +129,7 @@ class ConditionEncoder(nn.Module): if m_type == "embedding": self.mat_emb = nn.Embedding(mat_vocab, m_emb_dim) elif m_type == "physical": - self.material_mlp = _make_axis_mlp( - MATERIAL_PHYS_DIM, m_emb_dim, material_cfg.get("n_layers", 1) - ) + self.material_mlp = _make_axis_mlp(MATERIAL_PHYS_DIM, m_emb_dim, material_cfg.get("n_layers", 1)) elif m_type != "onehot": raise ValueError(f"unknown conditioning.material.type {m_type!r}") @@ -157,9 +145,7 @@ class ConditionEncoder(nn.Module): if p_type == "embedding": return self.pdg_emb(cond_cat[:, 0]) if p_type == "physical": - particle_phys = cond_cont[ - :, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM - ] + particle_phys = cond_cont[:, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM] return self.particle_mlp(particle_phys) assert self._particle_topn_col is not None return F.one_hot( @@ -245,9 +231,7 @@ class Router(nn.Module): """(B, n_experts) soft weights, rows summing to 1.""" raise NotImplementedError - def combine_weights( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def combine_weights(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: """(B, n_experts) train-time expert-combination weights. Default (`gumbel=False`): identical to `gate()`. Opt-in @@ -265,16 +249,12 @@ class Router(nn.Module): """(B,) hard expert index, used for eval-time grouped dispatch.""" return self.gate(cond_cont, cond_cat).argmax(dim=-1) - def balance_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def balance_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: """Importance CV^2 load-balancing auxiliary loss (Shazeer et al. 2017).""" importance = self.gate(cond_cont, cond_cat).sum(dim=0) # (n_experts,) return (importance.std() / (importance.mean() + 1e-8)) ** 2 - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: """Optional supervised auxiliary loss shaping the router's own belief. Default: none (a scalar 0). Routers gating on an unobservable @@ -282,16 +262,12 @@ class Router(nn.Module): """ return torch.zeros((), device=cond_cont.device) - def entropy_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def entropy_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: """Optional auxiliary loss rewarding sharper (lower-entropy) routing.""" norm_entropy, _ = self.gate_stats(cond_cont, cond_cat) return norm_entropy - def gate_stats( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> tuple[torch.Tensor, torch.Tensor]: + def gate_stats(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """Diagnostics: `(norm_entropy, importance)` — see v0.2 docstring for the full explanation, unchanged in v0.3.0.""" gate = self.gate(cond_cont, cond_cat) # (B, n_experts) @@ -321,9 +297,7 @@ def build_router(name: str, n_experts: int, **kwargs) -> Router: in one config without special-casing. """ if name not in ROUTER_REGISTRY: - raise ValueError( - f"unknown router type {name!r}; available: {sorted(ROUTER_REGISTRY)}" - ) + raise ValueError(f"unknown router type {name!r}; available: {sorted(ROUTER_REGISTRY)}") cls = ROUTER_REGISTRY[name] accepted = set(inspect.signature(cls.__init__).parameters) - {"self", "n_experts"} filtered = {k: v for k, v in kwargs.items() if k in accepted} @@ -374,8 +348,7 @@ class EnergyRouter(Router): if learn_width or learn_temperature: if not (width_min_ratio < 1.0 < width_max_ratio): raise ValueError( - f"width_min_ratio ({width_min_ratio}) and width_max_ratio " - f"({width_max_ratio}) must bracket 1.0" + f"width_min_ratio ({width_min_ratio}) and width_max_ratio ({width_max_ratio}) must bracket 1.0" ) self._width_lo = width_min_ratio * temperature self._width_hi = width_max_ratio * temperature @@ -388,10 +361,7 @@ class EnergyRouter(Router): centers = torch.linspace(-2.0, 2.0, n_experts) else: if len(centers_init) != n_experts: - raise ValueError( - f"centers_init has {len(centers_init)} values, " - f"expected n_experts={n_experts}" - ) + raise ValueError(f"centers_init has {len(centers_init)} values, expected n_experts={n_experts}") centers = torch.tensor(list(centers_init), dtype=torch.float32) if learn_centers: self.centers = nn.Parameter(centers) @@ -436,9 +406,7 @@ class PdgRouter(Router): def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: e = self.pdg_emb(cond_cat[:, 0]) # (B, emb_dim) - d2 = ((e.unsqueeze(1) - self.centers.unsqueeze(0)) ** 2).sum( - -1 - ) # (B, n_experts) + d2 = ((e.unsqueeze(1) - self.centers.unsqueeze(0)) ** 2).sum(-1) # (B, n_experts) return torch.softmax(-d2 / self.temperature, dim=-1) @@ -477,9 +445,7 @@ class ProcessRouter(Router): def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: return torch.softmax(self.logits(cond_cont, cond_cat), dim=-1) - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: return F.cross_entropy(self.logits(cond_cont, cond_cat), labels) @@ -500,14 +466,10 @@ class ComposedRouter(Router): joint = self.routers[0].gate(cond_cont, cond_cat) # (B, n_0) for router in self.routers[1:]: g = router.gate(cond_cont, cond_cat) # (B, n_i) - joint = (joint.unsqueeze(-1) * g.unsqueeze(1)).flatten( - 1 - ) # (B, prod so far) + joint = (joint.unsqueeze(-1) * g.unsqueeze(1)).flatten(1) # (B, prod so far) return joint - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: total = torch.zeros((), device=cond_cont.device) for router in self.routers: total = total + router.classify_loss(cond_cont, cond_cat, labels) @@ -560,9 +522,7 @@ def _parse_composed_axes(router_cfg: dict) -> list[dict]: _VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process") -def _check_router_conditioning_compat( - router_types: list[str], particle_conditioning: str -) -> None: +def _check_router_conditioning_compat(router_types: list[str], particle_conditioning: str) -> None: """Reject a router axis that reintroduces a training-vocab PDG lookup under `conditioning.particle.type = "physical"`. @@ -596,16 +556,12 @@ def _build_router_from_cfg( shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab) if router_cfg["type"] == "composed": axes = _parse_composed_axes(router_cfg) - _check_router_conditioning_compat( - [a["type"] for a in axes], particle_conditioning - ) + _check_router_conditioning_compat([a["type"] for a in axes], particle_conditioning) router = build_composed_router(axes, **shared_vocab) router.gumbel = bool(router_cfg.get("gumbel", False)) return router _check_router_conditioning_compat([router_cfg["type"]], particle_conditioning) - router_kwargs = { - k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts") - } + router_kwargs = {k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts")} router_kwargs.setdefault("pdg_vocab", pdg_vocab) router_kwargs.setdefault("mat_vocab", mat_vocab) router = build_router(router_cfg["type"], router_cfg["n_experts"], **router_kwargs) @@ -637,9 +593,7 @@ class ExpertTrunk(nn.Module): ) -> None: super().__init__() self.input_proj = nn.Linear(in_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ResBlock(hidden_dim, cond_dim, dropout=dropout) for _ in range(n_blocks)] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, out_dim) def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: @@ -710,12 +664,7 @@ class MonolithicTrunk(Trunk): ) -> None: super().__init__() self.input_proj = nn.Linear(in_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_dim, dropout=dropout) - for _ in range(n_res_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_dim, dropout=dropout) for _ in range(n_res_blocks)]) self.out_proj = nn.Linear(hidden_dim, out_dim) def forward( @@ -745,12 +694,7 @@ class RoutedTrunk(Trunk): super().__init__() self.router = router self.experts = nn.ModuleList( - [ - ExpertTrunk( - in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout - ) - for _ in range(router.n_experts) - ] + [ExpertTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) for _ in range(router.n_experts)] ) def forward( @@ -760,9 +704,7 @@ class RoutedTrunk(Trunk): cond_cont: torch.Tensor, cond_cat: torch.Tensor, ) -> torch.Tensor: - return _route_forward( - self.experts, self.router, x, cond, cond_cont, cond_cat, self.training - ) + return _route_forward(self.experts, self.router, x, cond, cond_cont, cond_cat, self.training) def build_trunk( @@ -775,9 +717,7 @@ def build_trunk( dropout: float = 0.0, ) -> Trunk: if router is not None: - return RoutedTrunk( - router, in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout - ) + return RoutedTrunk(router, in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) return MonolithicTrunk(in_dim, out_dim, hidden_dim, n_res_blocks, cond_dim, dropout) @@ -845,13 +785,9 @@ class _CausalAttnBlock(nn.Module): def __init__(self, dim: int, n_heads: int, dropout: float = 0.0) -> None: super().__init__() self.norm1 = nn.LayerNorm(dim) - self.attn = nn.MultiheadAttention( - dim, n_heads, dropout=dropout, batch_first=True - ) + self.attn = nn.MultiheadAttention(dim, n_heads, dropout=dropout, batch_first=True) self.norm2 = nn.LayerNorm(dim) - self.mlp = nn.Sequential( - nn.Linear(dim, 4 * dim), nn.GELU(), nn.Linear(4 * dim, dim) - ) + self.mlp = nn.Sequential(nn.Linear(dim, 4 * dim), nn.GELU(), nn.Linear(4 * dim, dim)) def forward(self, x: torch.Tensor, causal_mask: torch.Tensor) -> torch.Tensor: h = self.norm1(x) @@ -860,9 +796,7 @@ class _CausalAttnBlock(nn.Module): x = x + self.mlp(self.norm2(x)) return x - def step( - self, x_new: torch.Tensor, kv_cache: torch.Tensor | None - ) -> tuple[torch.Tensor, torch.Tensor]: + def step(self, x_new: torch.Tensor, kv_cache: torch.Tensor | None) -> tuple[torch.Tensor, torch.Tensor]: """`x_new`: `(B, 1, dim)`, this position's input. `kv_cache`: `None` (first position) or `(B, T, dim)` — `norm1(x)` of every earlier position at this same block. Returns `(out, new_kv_cache)`, `out` @@ -900,15 +834,11 @@ class AttentionHistory(HistoryEncoder): `Stage2Autoregressive.history_step`. """ - def __init__( - self, in_dim: int, out_dim: int, n_heads: int = 4, n_layers: int = 2 - ) -> None: + def __init__(self, in_dim: int, out_dim: int, n_heads: int = 4, n_layers: int = 2) -> None: super().__init__() self.start = nn.Parameter(torch.zeros(in_dim)) self.in_proj = nn.Linear(in_dim, out_dim) - self.blocks = nn.ModuleList( - [_CausalAttnBlock(out_dim, n_heads) for _ in range(n_layers)] - ) + self.blocks = nn.ModuleList([_CausalAttnBlock(out_dim, n_heads) for _ in range(n_layers)]) def _embed(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor: start = self.start.view(1, 1, -1).expand_as(feat) @@ -959,9 +889,7 @@ def stage2_type_dim(particle_type_cfg: dict, emb_dim: int) -> int: return PARTICLE_PHYS_DIM if target == "physical" else emb_dim -def stage2_trunk_sec_dim( - particle_type_cfg: dict, generator: str, k_max: int, emb_dim: int -) -> int: +def stage2_trunk_sec_dim(particle_type_cfg: dict, generator: str, k_max: int, emb_dim: int) -> int: """`Stage2OneShot`'s trunk output width. `target = "physical"` is untouched from v0.2/today: @@ -1020,17 +948,13 @@ class Stage1Model(nn.Module): self.cond_enc = ( cond_enc if cond_enc is not None - else ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim - ) + else ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) ) has_time = generator in ("flow", "ddpm") self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None merged_cond_dim = (time_dim if has_time else 0) + cond_out_dim in_dim = noise_dim if generator == "wgan" else x_dim - self.trunk = build_trunk( - router, in_dim, x_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout - ) + self.trunk = build_trunk(router, in_dim, x_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout) self.n_sec_head = None if n_sec_head_k_max is not None: self.n_sec_head = nn.Sequential( @@ -1047,16 +971,10 @@ class Stage1Model(nn.Module): t: torch.Tensor | None = None, ) -> torch.Tensor: c_emb = self.cond_enc(cond_cont, cond_cat) - cond = ( - torch.cat([self.time_emb(t), c_emb], dim=-1) - if self.time_emb is not None - else c_emb - ) + cond = torch.cat([self.time_emb(t), c_emb], dim=-1) if self.time_emb is not None else c_emb return self.trunk(x_t, cond, cond_cont, cond_cat) - def predict_n_sec( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def predict_n_sec(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: """Return n_sec logits (B, K_MAX+1) from conditioning alone. Only valid on a migrated v0.2 checkpoint's Stage1Model — fresh v0.3.0 configs predict n_sec from Stage2OneShot instead.""" @@ -1127,9 +1045,7 @@ class Stage2OneShot(nn.Module): self.cond_enc = ( cond_enc if cond_enc is not None - else ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim - ) + else ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) ) self.context_adapter = ContextAdapter(x_dim, context_dim) self.fuse = nn.Sequential( @@ -1140,9 +1056,7 @@ class Stage2OneShot(nn.Module): self.time_emb = SinusoidalEmbedding(time_dim) if has_time else None merged_cond_dim = (time_dim if has_time else 0) + cond_out_dim in_dim = noise_dim if generator == "wgan" else sec_dim - self.trunk = build_trunk( - router, in_dim, sec_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout - ) + self.trunk = build_trunk(router, in_dim, sec_dim, hidden_dim, n_res_blocks, merged_cond_dim, dropout) self.n_sec_head = None if build_n_sec_head: self.n_sec_head = nn.Sequential( @@ -1162,9 +1076,7 @@ class Stage2OneShot(nn.Module): self._type_k_max = k_max self._type_emb_dim = emb_dim - def _cond_embed( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor - ) -> torch.Tensor: + def _cond_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor) -> torch.Tensor: base = self.cond_enc(cond_cont, cond_cat) ctx = self.context_adapter(stage1_out) return self.fuse(torch.cat([base, ctx], dim=-1)) @@ -1178,11 +1090,7 @@ class Stage2OneShot(nn.Module): t: torch.Tensor | None = None, ) -> torch.Tensor: c_emb = self._cond_embed(cond_cont, cond_cat, stage1_out) - cond = ( - torch.cat([self.time_emb(t), c_emb], dim=-1) - if self.time_emb is not None - else c_emb - ) + cond = torch.cat([self.time_emb(t), c_emb], dim=-1) if self.time_emb is not None else c_emb return self.trunk(x_t, cond, cond_cont, cond_cat) def predict_n_sec( @@ -1274,10 +1182,7 @@ class Stage2Autoregressive(nn.Module): ) -> None: super().__init__() if history not in ("markov", "attention"): - raise ValueError( - f"stage2_model.autoregressive.history={history!r} — must be " - "'markov' or 'attention'" - ) + raise ValueError(f"stage2_model.autoregressive.history={history!r} — must be 'markov' or 'attention'") self.history_kind = history self.generator_kind = generator self.noise_dim = noise_dim @@ -1289,9 +1194,7 @@ class Stage2Autoregressive(nn.Module): self.cond_enc = ( cond_enc if cond_enc is not None - else ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim - ) + else ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) ) self.context_adapter = ContextAdapter(x_dim, context_dim) self.base_fuse = nn.Sequential( @@ -1305,15 +1208,11 @@ class Stage2Autoregressive(nn.Module): history_dim = cond_out_dim hist_in_dim = CONT_SLOT_DIM + self.type_dim self.history_encoder: HistoryEncoder = ( - AttentionHistory( - hist_in_dim, history_dim, n_heads=attn_n_heads, n_layers=attn_n_layers - ) + AttentionHistory(hist_in_dim, history_dim, n_heads=attn_n_heads, n_layers=attn_n_layers) if history == "attention" else MarkovHistory(hist_in_dim, history_dim) ) - token_fuse_in = ( - cond_out_dim + context_dim + history_dim + 2 - ) # +2: remaining_frac, slot_idx + token_fuse_in = cond_out_dim + context_dim + history_dim + 2 # +2: remaining_frac, slot_idx self.token_fuse = nn.Sequential( nn.Linear(token_fuse_in, cond_out_dim), nn.SiLU(), @@ -1350,9 +1249,7 @@ class Stage2Autoregressive(nn.Module): nn.Linear(hidden_dim // 2, self.type_dim), ) - def _base_cond( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor - ) -> torch.Tensor: + def _base_cond(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor) -> torch.Tensor: base = self.cond_enc(cond_cont, cond_cat) ctx = self.context_adapter(stage1_out) return self.base_fuse(torch.cat([base, ctx], dim=-1)) @@ -1392,9 +1289,7 @@ class Stage2Autoregressive(nn.Module): return self.history_encoder.init_cache() return None - def history_step( - self, token_feat: torch.Tensor, has_prev: torch.Tensor, cache - ) -> tuple[torch.Tensor, object]: + def history_step(self, token_feat: torch.Tensor, has_prev: torch.Tensor, cache) -> tuple[torch.Tensor, object]: """One inference slot's worth of history encoding: advances `cache` (from `init_history_cache`, or a previous `history_step` call) by `token_feat`/`has_prev` (`(B, 1, ...)` — the just-emitted previous @@ -1445,9 +1340,7 @@ class Stage2Autoregressive(nn.Module): out = self.trunk(x_flat, cond_flat, cond_cont_flat, cond_cat_flat) return out.view(B, K, -1) - def predict_n_sec( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor - ) -> torch.Tensor: + def predict_n_sec(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor) -> torch.Tensor: if self.n_sec_head is None: raise RuntimeError( "this Stage2Autoregressive has no n_sec_head — it belongs to " @@ -1514,9 +1407,7 @@ class CriticModel(nn.Module): if stage not in ("stage1", "stage2"): raise ValueError(f"stage must be 'stage1' or 'stage2', got {stage!r}") self.stage = stage - self.cond_enc = ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim - ) + self.cond_enc = ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) if stage == "stage2": self.context_adapter = ContextAdapter(context_in_dim, context_dim) self.fuse = nn.Sequential( @@ -1524,12 +1415,7 @@ class CriticModel(nn.Module): nn.SiLU(), ) self.input_proj = nn.Linear(in_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_res_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_res_blocks)]) self.out_norm = nn.LayerNorm(hidden_dim) self.out_proj = nn.Linear(hidden_dim, 1) @@ -1641,9 +1527,7 @@ def _migrate_legacy_model_config(model_config: dict) -> dict: } -def migrate_legacy_state_dict( - old_stage1_sd: dict, old_stage2_sd: dict -) -> tuple[dict, dict]: +def migrate_legacy_state_dict(old_stage1_sd: dict, old_stage2_sd: dict) -> tuple[dict, dict]: """Remap a v0.2 checkpoint's (`DenoisingMLP`-or-`WGANGenerator`, `SecondaryDecoder`-or-`WGANSecondaryGenerator`) state dicts onto the new `(Stage1Model, Stage2OneShot)` module structure produced by @@ -1702,11 +1586,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: common representation. `false` (default) keeps v0.2 behaviour: independent instances with identical config but independent weights. """ - cfg = ( - model_config - if "stage1_model" in model_config - else _migrate_legacy_model_config(model_config) - ) + cfg = model_config if "stage1_model" in model_config else _migrate_legacy_model_config(model_config) pdg_vocab = cfg["pdg_vocab"] mat_vocab = cfg["mat_vocab"] conditioning = cfg["conditioning"] @@ -1718,9 +1598,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: cond_out_dim = conditioning.get("out_dim", 128) shared_cond_enc: ConditionEncoder | None = None if conditioning.get("share_stages"): - shared_cond_enc = ConditionEncoder( - pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim - ) + shared_cond_enc = ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} @@ -1728,15 +1606,11 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: if s1cfg.get("active", True): router_cfg = s1cfg.get("router") or {} if router_cfg.get("enabled"): - stage1_router = _build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, particle_conditioning - ) + stage1_router = _build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, particle_conditioning) generator = s1cfg.get("generator", "flow") gen_sub = s1cfg.get(generator, {}) or {} legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") - n_sec_head_k_max = ( - s2cfg.get("k_max", K_MAX) if legacy_owner == "stage1" else None - ) + n_sec_head_k_max = s2cfg.get("k_max", K_MAX) if legacy_owner == "stage1" else None result["stage1"] = Stage1Model( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, @@ -1762,9 +1636,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: if router_cfg.get("tie_to_stage1") and stage1_router is not None: stage2_router = stage1_router else: - stage2_router = _build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, particle_conditioning - ) + stage2_router = _build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, particle_conditioning) generator = s2cfg.get("generator", "wgan") gen_sub = s2cfg.get(generator, {}) or {} legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") @@ -1796,9 +1668,7 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: cond_enc=shared_cond_enc, ) else: - sec_dim = stage2_trunk_sec_dim( - particle_type_cfg, generator, k_max, particle_cfg["emb_dim"] - ) + sec_dim = stage2_trunk_sec_dim(particle_type_cfg, generator, k_max, particle_cfg["emb_dim"]) result["stage2"] = Stage2OneShot( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, @@ -1828,11 +1698,7 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: "wgan"` training. Training-only — never persisted for inference the way `build_models`'s pair is. `None` for a stage that's inactive or not WGAN.""" - cfg = ( - model_config - if "stage1_model" in model_config - else _migrate_legacy_model_config(model_config) - ) + cfg = model_config if "stage1_model" in model_config else _migrate_legacy_model_config(model_config) pdg_vocab = cfg["pdg_vocab"] mat_vocab = cfg["mat_vocab"] conditioning = cfg["conditioning"] @@ -1861,9 +1727,7 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: if s2cfg.get("active", True) and s2cfg.get("generator") == "wgan": k_max = s2cfg.get("k_max", K_MAX) particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} - in_dim = stage2_trunk_sec_dim( - particle_type_cfg, "wgan", k_max, particle_cfg["emb_dim"] - ) + in_dim = stage2_trunk_sec_dim(particle_type_cfg, "wgan", k_max, particle_cfg["emb_dim"]) result["stage2"] = CriticModel( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, diff --git a/giant/model/schedule.py b/giant/model/schedule.py index b8b3891..c63e018 100644 --- a/giant/model/schedule.py +++ b/giant/model/schedule.py @@ -11,9 +11,7 @@ class CosineSchedule: steps = np.arange(T + 1, dtype=np.float64) f = np.cos(((steps / T + s) / (1.0 + s)) * np.pi / 2.0) ** 2 alpha_bars = (f / f[0]).astype(np.float32) - betas = np.clip(1.0 - alpha_bars[1:] / alpha_bars[:-1], 0.0, 0.999).astype( - np.float32 - ) + betas = np.clip(1.0 - alpha_bars[1:] / alpha_bars[:-1], 0.0, 0.999).astype(np.float32) self.betas = torch.from_numpy(betas) self.alphas = torch.from_numpy((1.0 - betas)) diff --git a/giant/particles.py b/giant/particles.py index c6be4e9..d8fcdc1 100644 --- a/giant/particles.py +++ b/giant/particles.py @@ -58,9 +58,7 @@ def particle_mass_charge(pdg: int) -> tuple[float, float]: if _pdgid.is_nucleus(pdg): z, a = _pdgid.Z(pdg), _pdgid.A(pdg) if z is None or a is None: - raise ValueError( - f"PDG {pdg}: is_nucleus but Z/A decode failed" - ) from None + raise ValueError(f"PDG {pdg}: is_nucleus but Z/A decode failed") from None return float(a) * _AMU_MEV, float(z) raise ValueError( f"PDG code {pdg} could not be resolved via the `particle` package " @@ -109,9 +107,7 @@ def nearest_known_pdg(mass: np.ndarray, charge: np.ndarray, candidates) -> np.nd if len(resolved) == 0: raise ValueError("nearest_known_pdg: no resolvable candidates") codes = np.array([r[0] for r in resolved], dtype=np.int64) - table_log_mass = np.log( - np.array([r[1] for r in resolved], dtype=np.float64) + _LOG_EPS - ) + table_log_mass = np.log(np.array([r[1] for r in resolved], dtype=np.float64) + _LOG_EPS) table_charge = np.array([r[2] for r in resolved], dtype=np.float64) mass = np.asarray(mass, dtype=np.float64) @@ -180,10 +176,7 @@ def decode_topn_class( n_other = int(other_mask.sum()) if n_other: if not topn_map.other_members: - raise ValueError( - "decode_topn_class: 'other' class predicted but " - "topn_map.other_members is empty" - ) + raise ValueError("decode_topn_class: 'other' class predicted but topn_map.other_members is empty") members = np.array(list(topn_map.other_members.keys()), dtype=np.int64) counts = np.array(list(topn_map.other_members.values()), dtype=np.float64) if other_policy == "drop": diff --git a/giant/pipeline.py b/giant/pipeline.py index 843140d..b6367ee 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -73,21 +73,14 @@ def _seed_energy_router( if not active: return if energy_quantiles.size == 0: - echo( - " warning: no energy samples collected — EnergyRouter falls back to " - "default centers" - ) + echo(" warning: no energy samples collected — EnergyRouter falls back to default centers") return assert cond_norm.mean is not None and cond_norm.std is not None levels = np.linspace(0.0, 1.0, router_cfg["n_experts"]) raw_centers = setup_cache.energy_quantile_at(energy_quantiles, levels) - centers_init = (raw_centers - cond_norm.mean[energy_idx]) / cond_norm.std[ - energy_idx - ] + centers_init = (raw_centers - cond_norm.mean[energy_idx]) / cond_norm.std[energy_idx] router_cfg["centers_init"] = centers_init.astype(np.float32).tolist() - echo( - f" seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}" - ) + echo(f" seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}") def run_setup_stage( @@ -142,23 +135,14 @@ def run_setup_stage( if cache is not None: cache.event_index = (unique_ids, counts) - train_events, val_events = make_event_split( - unique_ids, val_fraction=val_fraction, seed=seed - ) + train_events, val_events = make_event_split(unique_ids, val_fraction=val_fraction, seed=seed) events_arr = np.array(sorted(train_events)) n_train_steps = setup_cache.n_train_steps_for_split(unique_ids, counts, events_arr) - echo( - f" {int(counts.sum()):,} steps | " - f"{len(train_events)} train events | " - f"{len(val_events)} val events" - ) + echo(f" {int(counts.sum()):,} steps | {len(train_events)} train events | {len(val_events)} val events") if cache is not None and cache.vocab is not None: pdg_map, mat_map = cache.vocab - echo( - f"vocabulary maps: cache hit ({len(pdg_map)} PDG codes, " - f"{len(mat_map)} materials)" - ) + echo(f"vocabulary maps: cache hit ({len(pdg_map)} PDG codes, {len(mat_map)} materials)") else: echo("building vocabulary maps …") pdg_map, mat_map = build_index_maps_from_files(files) @@ -174,11 +158,7 @@ def run_setup_stage( # need that generality. proc_map: dict[str, int] | None = None process_router_cfg = next( - ( - r - for r in (stage1_router, stage2_router) - if r.get("enabled") and r.get("type") == "process" - ), + (r for r in (stage1_router, stage2_router) if r.get("enabled") and r.get("type") == "process"), None, ) if process_router_cfg is not None: @@ -186,10 +166,7 @@ def run_setup_stage( cached_proc_map = cache.proc_maps.get(n_experts) if cache is not None else None if cached_proc_map is not None: proc_map = cached_proc_map - echo( - f"process vocabulary: cache hit ({len(proc_map)} labels, " - f"{n_experts} experts)" - ) + echo(f"process vocabulary: cache hit ({len(proc_map)} labels, {n_experts} experts)") else: echo("building process vocabulary …") proc_map = build_process_map_from_files(files, n_experts=n_experts) @@ -214,16 +191,11 @@ def run_setup_stage( cached = cache.topn_maps.get(cache_key) if cache is not None else None if cached is not None: pdg_topn_map = cached - echo( - f"pdg top-N map: cache hit ({len(pdg_topn_map.class_map)} codes, " - f"{n_classes} classes)" - ) + echo(f"pdg top-N map: cache hit ({len(pdg_topn_map.class_map)} codes, {n_classes} classes)") else: echo("building pdg top-N map …") pdg_topn_map = build_pdg_topn_map_from_files(files, n_classes=n_classes) - echo( - f" {len(pdg_topn_map.class_map)} pdg codes mapped to {n_classes} classes" - ) + echo(f" {len(pdg_topn_map.class_map)} pdg codes mapped to {n_classes} classes") if cache is not None: cache.topn_maps[cache_key] = pdg_topn_map @@ -234,29 +206,17 @@ def run_setup_stage( cached = cache.topn_maps.get(cache_key) if cache is not None else None if cached is not None: mat_topn_map = cached - echo( - f"material top-N map: cache hit ({len(mat_topn_map.class_map)} " - f"materials, {n_classes} classes)" - ) + echo(f"material top-N map: cache hit ({len(mat_topn_map.class_map)} materials, {n_classes} classes)") else: echo("building material top-N map …") - mat_topn_map = build_topn_map_from_files( - files, "material", n_classes=n_classes, cast=str - ) - echo( - f" {len(mat_topn_map.class_map)} materials mapped to {n_classes} classes" - ) + mat_topn_map = build_topn_map_from_files(files, "material", n_classes=n_classes, cast=str) + echo(f" {len(mat_topn_map.class_map)} materials mapped to {n_classes} classes") if cache is not None: cache.topn_maps[cache_key] = mat_topn_map - energy_router_active = any( - r.get("enabled") and r.get("type") == "energy" - for r in (stage1_router, stage2_router) - ) + energy_router_active = any(r.get("enabled") and r.get("type") == "energy" for r in (stage1_router, stage2_router)) energy_idx = 3 - norm_key = setup_cache.normalizer_key( - val_fraction, seed, particle_conditioning, material_conditioning - ) + norm_key = setup_cache.normalizer_key(val_fraction, seed, particle_conditioning, material_conditioning) entry = cache.normalizers.get(norm_key) if cache is not None else None if entry is not None: @@ -281,27 +241,23 @@ def run_setup_stage( # router against this same (val_fraction, seed, conditioning) key # never needs to rescan just to seed centers. collect_energy_sample = energy_router_active or cache is not None - energy_sampler = ( - _ReservoirSampler(capacity=100_000) if collect_energy_sample else None - ) + energy_sampler = _ReservoirSampler(capacity=100_000) if collect_energy_sample else None for i, path in enumerate(files): for chunk in iter_file_chunks(path, offset=event_id_offset(i), k_max=k_max): mask = sorted_membership(chunk["event_id"], events_arr) if not mask.any(): continue chunk_tr = {k: v[mask] for k, v in chunk.items()} - cond_cont, _, target_s1, n_sec, sec_cont, _proc, _, _, _ = ( - build_features( - chunk_tr, - pdg_map, - mat_map, - proc_map=proc_map, - require_secondaries=True, - particle_conditioning=particle_conditioning, - material_conditioning=material_conditioning, - sec_phys_only=True, - k_max=k_max, - ) + cond_cont, _, target_s1, n_sec, sec_cont, _proc, _, _, _ = build_features( + chunk_tr, + pdg_map, + mat_map, + proc_map=proc_map, + require_secondaries=True, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, + sec_phys_only=True, + k_max=k_max, ) cond_acc.update(cond_cont) tgt_acc.update(target_s1) @@ -423,9 +379,7 @@ def run_train_job( # The secondary type-index map depends on stage2_model.particle_type.target, # independently of conditioning's own onehot/embedding choice above # (physical stays untouched/None). - particle_type_target = ( - cfg["stage2_model"].get("particle_type", {}).get("target", "physical") - ) + particle_type_target = cfg["stage2_model"].get("particle_type", {}).get("target", "physical") if particle_type_target == "onehot": assert setup.pdg_topn_map is not None sec_type_class_map = setup.pdg_topn_map.class_map diff --git a/giant/rollout.py b/giant/rollout.py index bb6b9fd..087473d 100644 --- a/giant/rollout.py +++ b/giant/rollout.py @@ -120,9 +120,7 @@ def decode_secondary_identity( pdg_topn_map: "TopNMap | None", other_policy: str, rng: np.random.Generator | None, -) -> tuple[ - np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray | None -]: +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray | None]: """Decode Stage 2's raw (sec_cont, sec_type) output into physical secondary attributes, branching on `sec_decoder.particle_type_cfg`: @@ -150,14 +148,12 @@ def decode_secondary_identity( sec_E, sec_dir_world, sec_mass, sec_charge, sec_valid = decode_secondaries( sec_full, n_sec_np, e_sec, pre_dir, sec_phys_normalizer=sec_phys_norm ) - sec_pdg = nearest_known_pdg( - sec_mass.reshape(-1), sec_charge.reshape(-1), pdg_map.keys() - ).reshape(sec_mass.shape) + sec_pdg = nearest_known_pdg(sec_mass.reshape(-1), sec_charge.reshape(-1), pdg_map.keys()).reshape( + sec_mass.shape + ) return sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, None - sec_E, sec_dir_world, sec_valid = decode_secondary_cont( - sec_cont.cpu().numpy(), n_sec_np, e_sec, pre_dir - ) + sec_E, sec_dir_world, sec_valid = decode_secondary_cont(sec_cont.cpu().numpy(), n_sec_np, e_sec, pre_dir) sec_type_np = sec_type.cpu().numpy() l1_dist = None @@ -182,12 +178,8 @@ def decode_secondary_identity( l1_dist = np.where(sec_valid, l1_dist, 0.0).astype(np.float32) sec_mass, sec_charge = particle_phys_array(sec_pdg.reshape(-1)).T - sec_mass = np.where(sec_valid, sec_mass.reshape(sec_pdg.shape), 0.0).astype( - np.float32 - ) - sec_charge = np.where(sec_valid, sec_charge.reshape(sec_pdg.shape), 0.0).astype( - np.float32 - ) + sec_mass = np.where(sec_valid, sec_mass.reshape(sec_pdg.shape), 0.0).astype(np.float32) + sec_charge = np.where(sec_valid, sec_charge.reshape(sec_pdg.shape), 0.0).astype(np.float32) sec_pdg = np.where(sec_valid, sec_pdg, 0).astype(np.int64) return sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg, l1_dist @@ -299,13 +291,9 @@ class _Recorder: RAM until the very end. """ - def __init__( - self, sink: Callable[[dict[str, np.ndarray]], None] | None = None - ) -> None: + def __init__(self, sink: Callable[[dict[str, np.ndarray]], None] | None = None) -> None: self._sink = sink - self._cols: dict[str, list] | None = ( - None if sink is not None else {k: [] for k in _RECORD_KEYS} - ) + self._cols: dict[str, list] | None = None if sink is not None else {k: [] for k in _RECORD_KEYS} self.n_rows = 0 self.termination_reason_counts: Counter[str] = Counter() @@ -313,10 +301,7 @@ class _Recorder: n = len(cols["event_id"]) if n == 0: return - row = { - k: np.asarray(cols[k], dtype=_RECORD_DTYPES[k]).reshape(n) - for k in _RECORD_KEYS - } + row = {k: np.asarray(cols[k], dtype=_RECORD_DTYPES[k]).reshape(n) for k in _RECORD_KEYS} self.n_rows += n reasons = row["termination_reason"] nonempty = reasons[reasons != ""] @@ -333,8 +318,7 @@ class _Recorder: def to_dict(self) -> dict[str, np.ndarray]: assert self._cols is not None, ( - "to_dict() is unavailable when streaming to a sink — use " - "n_rows/termination_reason_counts instead" + "to_dict() is unavailable when streaming to a sink — use n_rows/termination_reason_counts instead" ) out = {} for k, chunks in self._cols.items(): @@ -619,33 +603,19 @@ def _step_chunk( # --- Pre-step termination gates (in priority order; each track picks one) --- stop = np.zeros(n, dtype=bool) escaped_sel = escaped & ~stop - rec.add( - **_terminal_rows( - tr, escaped_sel, TERM_ESCAPED, edep=np.zeros(int(escaped_sel.sum())) - ) - ) + rec.add(**_terminal_rows(tr, escaped_sel, TERM_ESCAPED, edep=np.zeros(int(escaped_sel.sum())))) stop |= escaped_sel unknown_sel = ~known_pdg & ~stop - rec.add( - **_terminal_rows( - tr, unknown_sel, TERM_UNKNOWN_PDG, edep=tr["pre_E"][unknown_sel] - ) - ) + rec.add(**_terminal_rows(tr, unknown_sel, TERM_UNKNOWN_PDG, edep=tr["pre_E"][unknown_sel])) stop |= unknown_sel cutoff_sel = (tr["pre_E"] < energy_cutoff) & ~stop - rec.add( - **_terminal_rows( - tr, cutoff_sel, TERM_ENERGY_CUTOFF, edep=tr["pre_E"][cutoff_sel] - ) - ) + rec.add(**_terminal_rows(tr, cutoff_sel, TERM_ENERGY_CUTOFF, edep=tr["pre_E"][cutoff_sel])) stop |= cutoff_sel maxstep_sel = (tr["step_in_track"] >= max_steps) & ~stop - rec.add( - **_terminal_rows(tr, maxstep_sel, TERM_MAX_STEPS, edep=tr["pre_E"][maxstep_sel]) - ) + rec.add(**_terminal_rows(tr, maxstep_sel, TERM_MAX_STEPS, edep=tr["pre_E"][maxstep_sel])) stop |= maxstep_sel active = ~stop @@ -679,41 +649,27 @@ def _step_chunk( cond_norm, particle_conditioning=particle_conditioning, material_conditioning=material_conditioning, - pdg_topn_map=pdg_topn_map.class_map - if particle_conditioning == "onehot" - else None, - mat_topn_map=mat_topn_map.class_map - if material_conditioning == "onehot" - else None, + pdg_topn_map=pdg_topn_map.class_map if particle_conditioning == "onehot" else None, + mat_topn_map=mat_topn_map.class_map if material_conditioning == "onehot" else None, ) cc = torch.from_numpy(cond_cont).float().to(device) ck = torch.from_numpy(cond_cat).long().to(device) - stage1_norm, n_sec_pred_stage1 = sample_stage1( - stage1_model, cc, ck, steps, stage1_ddpm_steps - ) + stage1_norm, n_sec_pred_stage1 = sample_stage1(stage1_model, cc, ck, steps, stage1_ddpm_steps) raw = tgt_norm.inverse_transform(stage1_norm.cpu().numpy()) step_length = inv_log_transform(raw[:, 0]) edep, e_sec, post_E, _delta = energy_simplex_decode(raw[:, 1:3], tr["pre_E"]) post_dir_local = raw[:, 3:6].copy() - post_dir_local /= np.clip( - np.linalg.norm(post_dir_local, axis=1, keepdims=True), 1e-8, None - ) + post_dir_local /= np.clip(np.linalg.norm(post_dir_local, axis=1, keepdims=True), 1e-8, None) post_dir_world = inv_local_frame_rotation(tr["pre_dir"], post_dir_local) travel_dir_local = raw[:, 6:9].copy() - travel_dir_local /= np.clip( - np.linalg.norm(travel_dir_local, axis=1, keepdims=True), 1e-8, None - ) - post_pos = reconstruct_post_pos( - tr["pre_pos"], tr["pre_dir"], step_length, travel_dir_local - ) + travel_dir_local /= np.clip(np.linalg.norm(travel_dir_local, axis=1, keepdims=True), 1e-8, None) + post_pos = reconstruct_post_pos(tr["pre_pos"], tr["pre_dir"], step_length, travel_dir_local) - n_sec_pred = resolve_n_sec( - stage1_model, sec_decoder, cc, ck, stage1_norm, n_sec_pred_stage1 - ) + n_sec_pred = resolve_n_sec(stage1_model, sec_decoder, cc, ck, stage1_norm, n_sec_pred_stage1) n_sec_np = n_sec_pred.cpu().numpy().astype(np.int64) # --- Secondaries --- @@ -724,23 +680,19 @@ def _step_chunk( # decode_secondary_identity's docstring for how each # particle_type.target differs on whether PDG resolution is a real # identity decision or just a reporting label. - sec_cont, sec_type, _valid = sample_stage2( - sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps - ) - sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, sec_type_l1_dist = ( - decode_secondary_identity( - sec_decoder, - sec_cont, - sec_type, - n_sec_np, - e_sec, - tr["pre_dir"], - sec_phys_norm, - pdg_map, - pdg_topn_map, - other_policy, - rng, - ) + sec_cont, sec_type, _valid = sample_stage2(sec_decoder, cc, ck, stage1_norm, n_sec_pred, steps) + sec_E, sec_dir_world, sec_mass, sec_charge, sec_pdg_code, sec_type_l1_dist = decode_secondary_identity( + sec_decoder, + sec_cont, + sec_type, + n_sec_np, + e_sec, + tr["pre_dir"], + sec_phys_norm, + pdg_map, + pdg_topn_map, + other_policy, + rng, ) sec_valid = np.arange(sec_E.shape[1])[None, :] < n_sec_np[:, None] if l1_dist_collector is not None and sec_type_l1_dist is not None: diff --git a/giant/sample.py b/giant/sample.py index 3683384..d4486b2 100644 --- a/giant/sample.py +++ b/giant/sample.py @@ -67,9 +67,7 @@ def sample_ddpm( alpha = schedule.alphas[i] alpha_bar = schedule.alpha_bars[i] z = torch.randn_like(x) if i > 0 else torch.zeros_like(x) - x = (1.0 / alpha.sqrt()) * ( - x - (1.0 - alpha) / (1.0 - alpha_bar).sqrt() * eps_pred - ) + beta.sqrt() * z + x = (1.0 / alpha.sqrt()) * (x - (1.0 - alpha) / (1.0 - alpha_bar).sqrt() * eps_pred) + beta.sqrt() * z return x, _predict_n_sec_if_owned(model, cond_cont, cond_cat) @@ -173,9 +171,7 @@ def _decode_stage2_flat( else: sec_cont = x.view(B, k_max, CONT_SLOT_DIM) sec_type = sec_decoder.predict_type(cond_cont, cond_cat, stage1_out) - sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze( - 1 - ) + sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze(1) return sec_cont, sec_type, sec_valid @@ -206,9 +202,7 @@ def sample_secondaries( v = sec_decoder(x, cond_cont, cond_cat, stage1_out, t=t) x = x + v * dt - return _decode_stage2_flat( - sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred - ) + return _decode_stage2_flat(sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred) @torch.no_grad() @@ -226,9 +220,7 @@ def sample_secondaries_wgan( B = cond_cont.size(0) z = torch.randn(B, sec_decoder.noise_dim, device=cond_cont.device) x = sec_decoder(z, cond_cont, cond_cat, stage1_out) - return _decode_stage2_flat( - sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred - ) + return _decode_stage2_flat(sec_decoder, x, cond_cont, cond_cat, stage1_out, n_sec_pred) @torch.no_grad() @@ -306,12 +298,8 @@ def sample_secondaries_ar( has_prev = torch.full((B, 1), k >= 1, dtype=torch.bool, device=device) history_feat = prev_repr.unsqueeze(1) # (B, 1, CONT_SLOT_DIM + type_dim) remaining_frac = remaining.unsqueeze(1) # (B, 1) - slot_idx = torch.full( - (B, 1), k / max(k_max - 1, 1), device=device, dtype=torch.float32 - ) - hist, history_cache = sec_decoder.history_step( - history_feat, has_prev, history_cache - ) + slot_idx = torch.full((B, 1), k / max(k_max - 1, 1), device=device, dtype=torch.float32) + hist, history_cache = sec_decoder.history_step(history_feat, has_prev, history_cache) if generator == "wgan": z = torch.randn(B, 1, sec_decoder.noise_dim, device=device) @@ -366,21 +354,15 @@ def sample_secondaries_ar( sec_type[:, k] = type_k if target == "onehot": - type_for_history = F.one_hot( - type_k.argmax(dim=-1), num_classes=type_dim - ).float() + type_for_history = F.one_hot(type_k.argmax(dim=-1), num_classes=type_dim).float() else: type_for_history = type_k stick_fraction = torch.sigmoid(cont_k[:, 0]) - prev_repr = torch.cat( - [stick_fraction.unsqueeze(-1), cont_k[:, 1:4], type_for_history], dim=-1 - ) + prev_repr = torch.cat([stick_fraction.unsqueeze(-1), cont_k[:, 1:4], type_for_history], dim=-1) remaining = torch.clamp(remaining * (1.0 - stick_fraction), min=0.0) - sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze( - 1 - ) + sec_valid = torch.arange(k_max, device=device).unsqueeze(0) < n_sec_pred.unsqueeze(1) return sec_cont, sec_type, sec_valid @@ -426,16 +408,10 @@ def sample_stage2( case, so there's nothing to dispatch to here. """ if isinstance(sec_decoder, Stage2Autoregressive): - return sample_secondaries_ar( - sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps - ) + return sample_secondaries_ar(sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps) if sec_decoder.generator_kind == "wgan": - return sample_secondaries_wgan( - sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred - ) - return sample_secondaries( - sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps - ) + return sample_secondaries_wgan(sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred) + return sample_secondaries(sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=steps) def resolve_n_sec( diff --git a/giant/training/loop.py b/giant/training/loop.py index b2cb192..ca43651 100644 --- a/giant/training/loop.py +++ b/giant/training/loop.py @@ -78,9 +78,7 @@ def _try_validate_marginals(trainer: StageTrainer, val_loader, device, **kwargs) return validate_marginals(model, val_loader, device=device, **kwargs) -def _marginal_kl( - trainers: dict[str, StageTrainer], val_loader, device, **kwargs -) -> float: +def _marginal_kl(trainers: dict[str, StageTrainer], val_loader, device, **kwargs) -> float: """Mean marginal KL over the stage-1 sampling chain, or NaN when stage 1 is inactive or `validate_marginals` declined to produce a result.""" stage1 = trainers.get("stage1") @@ -90,9 +88,7 @@ def _marginal_kl( stage1, val_loader, device, - sec_decoder=trainers["stage2"].sampling_model() - if "stage2" in trainers - else None, + sec_decoder=trainers["stage2"].sampling_model() if "stage2" in trainers else None, **kwargs, ) if result is None: @@ -137,9 +133,7 @@ def train( trainers = build_stage_trainers(cfg, models, critics, device, total_train_batches) if not trainers: - raise ValueError( - "no active stage — stage1_model.active and stage2_model.active are both false" - ) + raise ValueError("no active stage — stage1_model.active and stage2_model.active are both false") has_adversarial = any(not tr.supports_val_loss for tr in trainers.values()) checkpoint_extras = { @@ -147,12 +141,8 @@ def train( "pdg_map": pdg_map, "mat_map": mat_map, "proc_map": proc_map, - "pdg_topn_map": topnmap_to_json(pdg_topn_map) - if pdg_topn_map is not None - else None, - "mat_topn_map": topnmap_to_json(mat_topn_map) - if mat_topn_map is not None - else None, + "pdg_topn_map": topnmap_to_json(pdg_topn_map) if pdg_topn_map is not None else None, + "mat_topn_map": topnmap_to_json(mat_topn_map) if mat_topn_map is not None else None, "model_config": model_config, } @@ -166,10 +156,7 @@ def train( best_val_loss = ckpt.get("best_val_loss", float("inf")) global_step = ckpt.get("global_step", 0) if start_epoch > epochs: - print( - f"checkpoint already completed epoch {start_epoch - 1} " - f"(>= --epochs {epochs}) — nothing to train" - ) + print(f"checkpoint already completed epoch {start_epoch - 1} (>= --epochs {epochs}) — nothing to train") return collector = MetricsCollector.create( @@ -206,10 +193,7 @@ def train( for batch in bar: B = batch[0].size(0) collector.add_train_batch( - { - name: trainer.step(batch, device, global_step) - for name, trainer in trainers.items() - }, + {name: trainer.step(batch, device, global_step) for name, trainer in trainers.items()}, B, ) bar.set_postfix_str(collector.postfix(), refresh=False) @@ -220,9 +204,7 @@ def train( bar.close() if shutdown.requested: - ckpt = build_checkpoint( - trainers, epoch - 1, global_step, best_val_loss, checkpoint_extras - ) + ckpt = build_checkpoint(trainers, epoch - 1, global_step, best_val_loss, checkpoint_extras) torch.save(ckpt, out_dir / "last.pt") last_completed_epoch = epoch - 1 print( @@ -244,15 +226,10 @@ def train( break B = batch[0].size(0) collector.add_val_batch( - { - name: tr.val_loss(batch, device) - for name, tr in scored.items() - }, + {name: tr.val_loss(batch, device) for name, tr in scored.items()}, B, ) - collector.observe_routers( - batch[0].to(device), batch[1].to(device), B - ) + collector.observe_routers(batch[0].to(device), batch[1].to(device), B) # An adversarial stage has no averageable validation loss, so it # needs the marginal-KL signal every epoch to pick a best @@ -264,10 +241,7 @@ def train( elif validate_every > 0 and epoch % validate_every == 0: stage1 = trainers.get("stage1") ddpm_steps = 1000 - if ( - isinstance(stage1, FlowDDPMStageTrainer) - and stage1.ddpm_schedule is not None - ): + if isinstance(stage1, FlowDDPMStageTrainer) and stage1.ddpm_schedule is not None: ddpm_steps = stage1.ddpm_schedule.T marginal_kl = _marginal_kl( trainers, @@ -292,22 +266,16 @@ def train( collector.set("val/marginal_kl", marginal_kl) collector.set( "gpu_mem_mb", - torch.cuda.max_memory_allocated(device) / (1024 * 1024) - if device.type == "cuda" - else 0.0, - ) - collector.set( - "samples_per_sec", collector.train_samples / max(epoch_time, 1e-8) + torch.cuda.max_memory_allocated(device) / (1024 * 1024) if device.type == "cuda" else 0.0, ) + collector.set("samples_per_sec", collector.train_samples / max(epoch_time, 1e-8)) collector.set("is_best", int(is_best)) collector.set("epoch_time_s", epoch_time) print(collector.summary_line(val_loss, epoch_time, is_best)) collector.write_epoch(global_step) - ckpt = build_checkpoint( - trainers, epoch, global_step, best_val_loss, checkpoint_extras - ) + ckpt = build_checkpoint(trainers, epoch, global_step, best_val_loss, checkpoint_extras) if is_best: best_val_loss = val_loss ckpt["best_val_loss"] = best_val_loss diff --git a/giant/training/metrics.py b/giant/training/metrics.py index 42975ee..d1fb0dd 100644 --- a/giant/training/metrics.py +++ b/giant/training/metrics.py @@ -116,11 +116,7 @@ class _RouterAccumulator: def add(self, entropy: torch.Tensor, importance: torch.Tensor, n: int) -> None: self.entropy += entropy.item() * n - self.importance = ( - importance.clone() - if self.importance is None - else self.importance + importance - ) + self.importance = importance.clone() if self.importance is None else self.importance + importance self.n += n def stats(self) -> dict[str, float]: @@ -168,22 +164,18 @@ class MetricsCollector: self._train = {name: _Accumulator() for name in trainers} self._val = {name: _Accumulator() for name in trainers} self._routers = { - name: _RouterAccumulator(tr.router.n_experts) - for name, tr in trainers.items() - if tr.router is not None + name: _RouterAccumulator(tr.router.n_experts) for name, tr in trainers.items() if tr.router is not None } # Only "mean" specs need summing; "last" specs are read straight off # the accumulator's most recent stats dict. "grad_norm" is always # summed — it feeds the run-level `grad_norm` column whether or not # a trainer reports it per stage. self._train_keys = { - name: {spec.key for spec in tr.train_metrics if spec.reduce == "mean"} - | {"grad_norm"} + name: {spec.key for spec in tr.train_metrics if spec.reduce == "mean"} | {"grad_norm"} for name, tr in trainers.items() } self._val_keys = { - name: {spec.key for spec in tr.val_metrics if spec.reduce == "mean"} - for name, tr in trainers.items() + name: {spec.key for spec in tr.val_metrics if spec.reduce == "mean"} for name, tr in trainers.items() } self._run_values: dict[str, float] = {} self._epoch = 0 @@ -224,13 +216,9 @@ class MetricsCollector: import wandb except ImportError as exc: raise RuntimeError( - "train.wandb = true (--wandb) requires the 'wandb' package — " - "install it via `uv sync --extra wandb`" + "train.wandb = true (--wandb) requires the 'wandb' package — install it via `uv sync --extra wandb`" ) from exc - param_counts = { - name: sum(p.numel() for p in tr.model.parameters()) - for name, tr in trainers.items() - } + param_counts = {name: sum(p.numel() for p in tr.model.parameters()) for name, tr in trainers.items()} param_counts["total"] = sum(param_counts.values()) wandb_run = wandb.init( project=wandb_project, @@ -290,9 +278,7 @@ class MetricsCollector: self._batch_grad_norm += stage_stats.get("grad_norm", 0.0) if self._ema_seeded: self._ema_loss += _EMA_ALPHA * (self._batch_loss - self._ema_loss) - self._ema_grad_norm += _EMA_ALPHA * ( - self._batch_grad_norm - self._ema_grad_norm - ) + self._ema_grad_norm += _EMA_ALPHA * (self._batch_grad_norm - self._ema_grad_norm) else: self._ema_loss = self._batch_loss self._ema_grad_norm = self._batch_grad_norm @@ -303,9 +289,7 @@ class MetricsCollector: self._val[name].add(stage_stats, self._val_keys[name], batch_size) @torch.no_grad() - def observe_routers( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, batch_size: int - ) -> None: + def observe_routers(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, batch_size: int) -> None: """Record gate diagnostics for every routed stage on this batch. Called from the validation pass only (as in v0.2/v0.3.0), so a stage @@ -339,9 +323,7 @@ class MetricsCollector: payload[f"batch/{name}/lr"] = trainer.optimizer.param_groups[0]["lr"] if trainer.router is not None: with torch.no_grad(): - entropy, _ = trainer.router.gate_stats( - batch[0].to(device), batch[1].to(device) - ) + entropy, _ = trainer.router.gate_stats(batch[0].to(device), batch[1].to(device)) payload[f"batch/{name}/router/entropy"] = entropy.item() self.wandb_run.log(payload, step=global_step) @@ -367,10 +349,7 @@ class MetricsCollector: self._run_values[column] = value def summary_line(self, val_loss: float, epoch_time: float, is_best: bool) -> str: - bits = [ - trainer.summary(self.train_means(name)) - for name, trainer in self.trainers.items() - ] + bits = [trainer.summary(self.train_means(name)) for name, trainer in self.trainers.items()] marker = " [best]" if is_best else "" return ( f"epoch {self._epoch:{self.epoch_width}d}/{self.epochs} " diff --git a/giant/training/stage2_inputs.py b/giant/training/stage2_inputs.py index b23ac5d..e2f8367 100644 --- a/giant/training/stage2_inputs.py +++ b/giant/training/stage2_inputs.py @@ -100,9 +100,9 @@ def _assemble_stage2_real( `Stage2Autoregressive`'s per-token target also uses; the two must stay in lockstep. See `_assemble_stage2_ar_target`'s docstring for the (target, generator) width rules.""" - return _assemble_stage2_ar_target( - sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim - ).flatten(1) + return _assemble_stage2_ar_target(sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim).flatten( + 1 + ) def _stick_fraction(sec_cont: torch.Tensor) -> torch.Tensor: @@ -137,18 +137,14 @@ def _ar_has_prev(k_max: int, device: torch.device) -> torch.Tensor: return (torch.arange(k_max, device=device) >= 1).unsqueeze(0) -def _ar_meta( - k_max: int, batch: int, device: torch.device, fraction: torch.Tensor -) -> dict[str, torch.Tensor]: +def _ar_meta(k_max: int, batch: int, device: torch.device, fraction: torch.Tensor) -> dict[str, torch.Tensor]: """`has_prev`/`remaining_frac`/`slot_idx` — the three per-token AR conditioning tensors that don't depend on *which* history representation (ground truth vs. the scheduled-sampling mix) produced `fraction`. Shared by `_assemble_stage2_ar_inputs` and `_assemble_stage2_ar_inputs_scheduled`, which differ only in `history_feat`.""" - slot_idx = ( - torch.arange(k_max, device=device).float() / max(k_max - 1, 1) - ).unsqueeze(0) + slot_idx = (torch.arange(k_max, device=device).float() / max(k_max - 1, 1)).unsqueeze(0) return { "has_prev": _ar_has_prev(k_max, device).expand(batch, -1), "remaining_frac": _remaining_energy_fraction(fraction), @@ -182,9 +178,7 @@ def _assemble_stage2_ar_inputs( return {"history_feat": history_feat, **_ar_meta(K, B, device, fraction)} -def _stage2_tf_prob( - mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int -) -> float: +def _stage2_tf_prob(mode: str, p_start: float, p_end: float, epoch: int, total_epochs: int) -> float: """P(condition slot k+1 on the TRUE token k rather than the model's own prediction), for the current epoch (`stage2_model.autoregressive.teacher_forcing`). @@ -260,9 +254,7 @@ def _assemble_stage2_ar_inputs_scheduled( device = sec_cont.device B, K = sec_cont.shape[0], sec_cont.shape[1] if p_tf >= 1.0: - return _assemble_stage2_ar_inputs( - sec_cont, sec_type_idx, particle_type_cfg, cond_enc, emb_dim - ) + return _assemble_stage2_ar_inputs(sec_cont, sec_type_idx, particle_type_cfg, cond_enc, emb_dim) was_training = model.training sec_cont_pred, sec_type_pred, _ = sample_secondaries_ar( @@ -273,9 +265,7 @@ def _assemble_stage2_ar_inputs_scheduled( fraction_gt = _stick_fraction(sec_cont) dir_gt = sec_cont[..., 1:CONT_SLOT_DIM] - type_repr_gt = _type_repr( - sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim - ) + type_repr_gt = _type_repr(sec_type_idx, sec_cont, particle_type_cfg, cond_enc, emb_dim) fraction_pred, dir_pred, type_repr_pred = _history_repr_from_ar_sample( sec_cont_pred, sec_type_pred, particle_type_cfg ) @@ -323,8 +313,6 @@ def _relax_onehot_type_slice( cont, type_logits = x[..., :cont_dim], x[..., cont_dim:] if grad_probe is not None: cont.register_hook(lambda g: grad_probe.__setitem__("cont", g.norm().item())) - type_logits.register_hook( - lambda g: grad_probe.__setitem__("type", g.norm().item()) - ) + type_logits.register_hook(lambda g: grad_probe.__setitem__("type", g.norm().item())) type_soft = F.gumbel_softmax(type_logits, tau=tau, hard=True, dim=-1) return torch.cat([cont, type_soft], dim=-1).reshape(B, -1) diff --git a/giant/training/trainers.py b/giant/training/trainers.py index e1ac249..8473a87 100644 --- a/giant/training/trainers.py +++ b/giant/training/trainers.py @@ -41,9 +41,7 @@ from giant.training.stage2_inputs import ( @torch.no_grad() -def _update_ema( - ema_model: torch.nn.Module, model: torch.nn.Module, decay: float -) -> None: +def _update_ema(ema_model: torch.nn.Module, model: torch.nn.Module, decay: float) -> None: for ema_p, p in zip(ema_model.parameters(), model.parameters()): ema_p.mul_(decay).add_(p, alpha=1 - decay) @@ -129,9 +127,7 @@ class StageSpec: type_gumbel_tau_end: float = 0.1 @classmethod - def from_config( - cls, cfg: dict, name: str, is_stage2: bool, steps_per_epoch: int - ) -> "StageSpec": + def from_config(cls, cfg: dict, name: str, is_stage2: bool, steps_per_epoch: int) -> "StageSpec": t = cfg["train"] stage_cfg = cfg[f"{name}_model"] router_cfg = stage_cfg.get("router") or {} @@ -144,8 +140,7 @@ class StageSpec: decoder=stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot", lambda_weight=stage_cfg.get("lambda", 1.0), n_sec_lambda=cfg["stage2_model"].get("n_sec", {}).get("lambda", 0.1), - particle_type=cfg["stage2_model"].get("particle_type") - or {"target": "physical"}, + particle_type=cfg["stage2_model"].get("particle_type") or {"target": "physical"}, particle_type_emb_dim=cfg["conditioning"]["particle"]["emb_dim"], # train.* keys are all guaranteed by DEFAULT_CONFIG's deep-merge # (giant/config.py), so they read directly; the field defaults @@ -244,9 +239,7 @@ class StageTrainer: # --- schedule ------------------------------------------------------- - def _init_lr_schedule( - self, optimizer: optim.Optimizer, warmup_steps: int, total_steps: int - ) -> None: + def _init_lr_schedule(self, optimizer: optim.Optimizer, warmup_steps: int, total_steps: int) -> None: self._lr_lambda = _cosine_warmup_lambda(warmup_steps, total_steps) self.total_steps = total_steps self.lr_sched = optim.lr_scheduler.LambdaLR(optimizer, self._lr_lambda) @@ -270,9 +263,7 @@ class StageTrainer: """This stage's fragment of the end-of-epoch console line.""" raise NotImplementedError - def val_objective( - self, train_means: dict, val_means: dict, marginal_kl: float - ) -> float: + def val_objective(self, train_means: dict, val_means: dict, marginal_kl: float) -> float: """This stage's contribution to the best-checkpoint selection score.""" raise NotImplementedError @@ -359,9 +350,7 @@ class StageTrainer: return target.flatten(1) if flatten else target @staticmethod - def _sec_mask( - n_sec: torch.Tensor, k_max: int, device: torch.device - ) -> torch.Tensor: + def _sec_mask(n_sec: torch.Tensor, k_max: int, device: torch.device) -> torch.Tensor: """`(B, K_MAX)` bool prefix mask: slot k is valid iff `k < n_sec`.""" return torch.arange(k_max, device=device).unsqueeze(0) < n_sec.unsqueeze(1) @@ -397,9 +386,7 @@ class StageTrainer: return l_nsec, nsec_acc @staticmethod - def _step_optimizer( - optimizer: optim.Optimizer, loss: torch.Tensor, params: list - ) -> float: + def _step_optimizer(optimizer: optim.Optimizer, loss: torch.Tensor, params: list) -> float: """`zero_grad -> backward -> clip_grad_norm_(1.0) -> step`, returning the pre-clip grad norm. The one place the grad-clip constant lives.""" optimizer.zero_grad() @@ -450,9 +437,7 @@ class StageTrainer: class FlowDDPMStageTrainer(StageTrainer): """flow or ddpm generator for a single stage.""" - def __init__( - self, spec: StageSpec, model: torch.nn.Module, device: torch.device - ) -> None: + def __init__(self, spec: StageSpec, model: torch.nn.Module, device: torch.device) -> None: if spec.is_stage2 and spec.generator not in ("flow",): raise NotImplementedError( f"stage2_model.generator={spec.generator!r} is accepted by the " @@ -466,26 +451,16 @@ class FlowDDPMStageTrainer(StageTrainer): # NotImplementedError above): "physical" keeps it folded in # (PARTICLE_PHYS_DIM wide, unchanged from v0.2); "onehot"/"embedding" # pull it out into model.type_head instead (0 here). - self._flow_type_dim = ( - None - if self.particle_type_cfg.get("target", "physical") == "physical" - else 0 - ) + self._flow_type_dim = None if self.particle_type_cfg.get("target", "physical") == "physical" else 0 self.params = list(self.model.parameters()) - self.optimizer = optim.AdamW( - self.params, lr=spec.lr, weight_decay=spec.weight_decay - ) + self.optimizer = optim.AdamW(self.params, lr=spec.lr, weight_decay=spec.weight_decay) self._init_lr_schedule( self.optimizer, warmup_steps=spec.warmup_epochs * spec.steps_per_epoch, total_steps=max(spec.epochs * spec.steps_per_epoch, 1), ) - self.ddpm_schedule = ( - CosineSchedule(T=spec.ddpm_n_steps).to(device) - if spec.generator == "ddpm" - else None - ) + self.ddpm_schedule = CosineSchedule(T=spec.ddpm_n_steps).to(device) if spec.generator == "ddpm" else None self.train_metrics = [ train_metric(key) @@ -515,9 +490,7 @@ class FlowDDPMStageTrainer(StageTrainer): ] self.stage_metrics = [stage_metric("lr")] - def _generator_loss( - self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None - ): + def _generator_loss(self, cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=None): if not self.is_stage2: if self.generator == "flow": return flow_matching_loss(self.model, x1_s1, cond_cont, cond_cat) @@ -584,22 +557,16 @@ class FlowDDPMStageTrainer(StageTrainer): mask = sec_mask.float() denom = mask.sum().clamp(min=1) if self.particle_type_cfg.get("target") == "onehot": - ce = F.cross_entropy( - type_out.transpose(1, 2), sec_type_idx, reduction="none" - ) + ce = F.cross_entropy(type_out.transpose(1, 2), sec_type_idx, reduction="none") l_type = (ce * mask).sum() / denom - type_acc = ( - (type_out.argmax(-1) == sec_type_idx).float() * mask - ).sum() / denom + type_acc = ((type_out.argmax(-1) == sec_type_idx).float() * mask).sum() / denom else: # "embedding" target_vec = self.model.cond_enc.pdg_emb(sec_type_idx).detach() se = ((type_out - target_vec) ** 2).mean(-1) l_type = (se * mask).sum() / denom return l_type, type_acc - def _compute( - self, batch: tuple, device: torch.device, epoch: int | None = None - ) -> dict: + def _compute(self, batch: tuple, device: torch.device, epoch: int | None = None) -> dict: """`epoch=None` (the `val_loss` path) always uses full teacher forcing (`p_tf=1.0`) regardless of `spec.teacher_forcing` — validation should stay a stable, non-stochastic ground-truth comparison; only @@ -619,23 +586,13 @@ class FlowDDPMStageTrainer(StageTrainer): x1_s2 = None ar_inputs = None if self.is_stage2 and self.decoder == "autoregressive": - ar_inputs = self._ar_inputs( - cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch - ) - x1_s2 = self._sec_target( - sec_cont, sec_type_idx, self.generator, flatten=False - ) + ar_inputs = self._ar_inputs(cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch) + x1_s2 = self._sec_target(sec_cont, sec_type_idx, self.generator, flatten=False) elif self.is_stage2: - x1_s2 = self._sec_target( - sec_cont, sec_type_idx, self.generator, flatten=True - ) + x1_s2 = self._sec_target(sec_cont, sec_type_idx, self.generator, flatten=True) - l_gen = self._generator_loss( - cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs - ) - l_nsec, nsec_acc = self._n_sec_loss( - cond_cont, cond_cat, stage1_ctx, n_sec, device - ) + l_gen = self._generator_loss(cond_cont, cond_cat, x1_s1, x1_s2, sec_mask, stage1_ctx, ar_inputs=ar_inputs) + l_nsec, nsec_acc = self._n_sec_loss(cond_cont, cond_cat, stage1_ctx, n_sec, device) l_type, type_acc = self._type_loss( cond_cont, @@ -653,11 +610,7 @@ class FlowDDPMStageTrainer(StageTrainer): l_proc = self.router.classify_loss(cond_cont, cond_cat, proc_idx) l_entropy = self.router.entropy_loss(cond_cont, cond_cat) - total = ( - self.spec.lambda_weight * l_gen - + self.spec.n_sec_lambda * l_nsec - + self.particle_type_lambda * l_type - ) + total = self.spec.lambda_weight * l_gen + self.spec.n_sec_lambda * l_nsec + self.particle_type_lambda * l_type if self.spec.lambda_balance > 0: total = total + self.spec.lambda_balance * l_balance if self.spec.lambda_proc > 0: @@ -698,9 +651,7 @@ class FlowDDPMStageTrainer(StageTrainer): @torch.no_grad() def val_loss(self, batch: tuple, device: torch.device) -> dict: - return { - key: value.item() for key, value in self._compute(batch, device).items() - } + return {key: value.item() for key, value in self._compute(batch, device).items()} # --- reporting ------------------------------------------------------ @@ -710,9 +661,7 @@ class FlowDDPMStageTrainer(StageTrainer): def summary(self, means: dict) -> str: return f"{self.name}[loss={means.get('loss', 0.0):.3f}]" - def val_objective( - self, train_means: dict, val_means: dict, marginal_kl: float - ) -> float: + def val_objective(self, train_means: dict, val_means: dict, marginal_kl: float) -> float: return val_means.get("loss", 0.0) @@ -799,15 +748,8 @@ class WGANStageTrainer(StageTrainer): if self.decoder == "autoregressive": epoch = global_step // self.spec.steps_per_epoch - ar = self._ar_inputs( - cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch - ) - real = ( - self._sec_target(sec_cont, sec_type_idx, "wgan", flatten=False).reshape( - B, -1 - ) - * mask - ) + ar = self._ar_inputs(cond_cont, cond_cat, stage1_ctx, sec_cont, sec_type_idx, n_sec, epoch) + real = self._sec_target(sec_cont, sec_type_idx, "wgan", flatten=False).reshape(B, -1) * mask z = torch.randn(B, k_max, self.model.noise_dim, device=device) fake_raw = self.model( z, @@ -891,9 +833,7 @@ class WGANStageTrainer(StageTrainer): # --- generator (+ n_sec) step --- did_g_step = global_step % self.n_critic == 0 - l_nsec, nsec_acc = self._n_sec_loss( - cond_cont, cond_cat, stage1_ctx, n_sec, device - ) + l_nsec, nsec_acc = self._n_sec_loss(cond_cont, cond_cat, stage1_ctx, n_sec, device) # On a non-generator-step batch with no n_sec_head on this stage # (n_sec now defaults to stage 2), there's nothing for @@ -902,9 +842,7 @@ class WGANStageTrainer(StageTrainer): skip_g_step = not did_g_step and self.model.n_sec_head is None if did_g_step: g_loss_adv = generator_loss(critic_fn, fake) - g_loss = ( - self.spec.lambda_weight * g_loss_adv + self.spec.n_sec_lambda * l_nsec - ) + g_loss = self.spec.lambda_weight * g_loss_adv + self.spec.n_sec_lambda * l_nsec else: g_loss_adv = torch.zeros((), device=device) g_loss = self.spec.n_sec_lambda * l_nsec @@ -941,14 +879,9 @@ class WGANStageTrainer(StageTrainer): return stats["d_loss"] + stats["g_loss"] def summary(self, means: dict) -> str: - return ( - f"{self.name}[d={means.get('d_loss', 0.0):.3f} " - f"g={means.get('g_loss', 0.0):.3f}]" - ) + return f"{self.name}[d={means.get('d_loss', 0.0):.3f} g={means.get('g_loss', 0.0):.3f}]" - def val_objective( - self, train_means: dict, val_means: dict, marginal_kl: float - ) -> float: + def val_objective(self, train_means: dict, val_means: dict, marginal_kl: float) -> float: """No monotone per-batch WGAN loss fit for averaging, so best-checkpoint selection uses the real marginal-KL signal when `validate_marginals` produced one, and falls back to this epoch's own @@ -1002,8 +935,7 @@ def build_stage_trainers( if spec.generator == "wgan": critic = critics.get(name) assert critic is not None, ( - f"{name}_model.generator='wgan' requires a critic (see " - "giant.model.network.build_critics)" + f"{name}_model.generator='wgan' requires a critic (see giant.model.network.build_critics)" ) trainers[name] = WGANStageTrainer(spec, model, critic, device) else: diff --git a/giant/validate.py b/giant/validate.py index c193f4f..1d3b5a0 100644 --- a/giant/validate.py +++ b/giant/validate.py @@ -8,9 +8,7 @@ from giant.sample import resolve_n_sec, sample_stage1, sample_stage2 _SEC_PHYS_NAMES = ["log_mass", "charge"] -def _histogram_kl( - p_samples: np.ndarray, q_samples: np.ndarray, bins: int = 50, eps: float = 1e-8 -) -> float: +def _histogram_kl(p_samples: np.ndarray, q_samples: np.ndarray, bins: int = 50, eps: float = 1e-8) -> float: """KL(P || Q) between two 1D samples, estimated via a shared histogram.""" lo = min(p_samples.min(), q_samples.min()) hi = max(p_samples.max(), q_samples.max()) @@ -33,9 +31,7 @@ def _bincount_frac(x: np.ndarray, minlength: int) -> np.ndarray: return counts / total if total > 0 else counts -def _categorical_kl( - real_idx: np.ndarray, gen_idx: np.ndarray, n_classes: int, eps: float = 1e-8 -) -> float: +def _categorical_kl(real_idx: np.ndarray, gen_idx: np.ndarray, n_classes: int, eps: float = 1e-8) -> float: """KL(P_real || Q_gen) between two class-index samples over `n_classes` categories, estimated from bincount fractions. NaN if either side has no valid samples (mirrors `_histogram_kl`'s empty-input handling).""" @@ -48,9 +44,7 @@ def _categorical_kl( return float(np.sum(p * np.log(p / q))) -def _embedding_nearest_class( - vectors: torch.Tensor, emb_weight: torch.Tensor -) -> np.ndarray: +def _embedding_nearest_class(vectors: torch.Tensor, emb_weight: torch.Tensor) -> np.ndarray: """Nearest row index (L1) of `vectors` (..., emb_dim) against `emb_weight` (vocab, emb_dim) — same computation as `giant.particles.decode_embedding_nearest`, but returning the raw class @@ -109,11 +103,7 @@ def validate_marginals( sec_decoder.eval() k_max = sec_decoder.k_max if sec_decoder is not None else 0 - target = ( - sec_decoder.particle_type_cfg.get("target", "physical") - if sec_decoder is not None - else "physical" - ) + target = sec_decoder.particle_type_cfg.get("target", "physical") if sec_decoder is not None else "physical" all_real, all_gen = [], [] all_n_sec_real, all_n_sec_pred = [], [] @@ -131,9 +121,7 @@ def validate_marginals( cond_cont = cond_cont.to(device) cond_cat = cond_cat.to(device) - gen, n_sec_pred = sample_stage1( - stage1_model, cond_cont, cond_cat, steps=steps, ddpm_steps=ddpm_steps - ) + gen, n_sec_pred = sample_stage1(stage1_model, cond_cont, cond_cat, steps=steps, ddpm_steps=ddpm_steps) all_real.append(x1.numpy()) all_gen.append(gen.cpu().numpy()) @@ -141,9 +129,7 @@ def validate_marginals( if sec_decoder is None: continue - n_sec_pred = resolve_n_sec( - stage1_model, sec_decoder, cond_cont, cond_cat, gen, n_sec_pred - ) + n_sec_pred = resolve_n_sec(stage1_model, sec_decoder, cond_cont, cond_cat, gen, n_sec_pred) n_sec_pred_np = n_sec_pred.cpu().numpy() n_sec_np = n_sec.numpy() all_n_sec_real.append(n_sec_np) @@ -155,9 +141,7 @@ def validate_marginals( sec_cont_pred, sec_type_pred, sec_valid_pred = sample_stage2( sec_decoder, cond_cont, cond_cat, gen, n_sec_pred, steps=steps ) - gen_frac = 1.0 / ( - 1.0 + np.exp(-sec_cont_pred[:, :, 0].cpu().numpy().astype(np.float64)) - ) + gen_frac = 1.0 / (1.0 + np.exp(-sec_cont_pred[:, :, 0].cpu().numpy().astype(np.float64))) gen_valid = sec_valid_pred.cpu().numpy() if target == "physical": @@ -182,17 +166,9 @@ def validate_marginals( real = np.concatenate(all_real, axis=0) generated = np.concatenate(all_gen, axis=0) - kl_divergence = np.array( - [ - _histogram_kl(real[:, j], generated[:, j], bins=kl_bins) - for j in range(real.shape[1]) - ] - ) + kl_divergence = np.array([_histogram_kl(real[:, j], generated[:, j], bins=kl_bins) for j in range(real.shape[1])]) - header = ( - f"{'Dim':<20} {'real_mean':>10} {'gen_mean':>10} " - f"{'real_std':>10} {'gen_std':>10} {'KL(real||gen)':>14}" - ) + header = f"{'Dim':<20} {'real_mean':>10} {'gen_mean':>10} {'real_std':>10} {'gen_std':>10} {'KL(real||gen)':>14}" print(f"\n{header}") print("-" * len(header)) for j, name in enumerate(LOCAL_TARGET_NAMES): @@ -215,10 +191,7 @@ def validate_marginals( n_sec_accuracy = float((n_sec_real == n_sec_pred_all).mean()) energy_fraction_kl = np.full(k_max, np.nan) - print( - f"\n{'n_sec':<20} accuracy={n_sec_accuracy:.4f} " - f"mean|Δ|={np.abs(n_sec_real - n_sec_pred_all).mean():.4f}" - ) + print(f"\n{'n_sec':<20} accuracy={n_sec_accuracy:.4f} mean|Δ|={np.abs(n_sec_real - n_sec_pred_all).mean():.4f}") n_sec_dist_header = f"{'n_sec value':<20} {'real_frac':>10} {'gen_frac':>10}" print(n_sec_dist_header) print("-" * len(n_sec_dist_header)) @@ -240,10 +213,7 @@ def validate_marginals( continue kl = _histogram_kl(r, g, bins=kl_bins) energy_fraction_kl[j] = kl - print( - f"{j:<24} {r.mean():>10.4f} {g.mean():>10.4f} " - f"{r.std():>10.4f} {g.std():>10.4f} {kl:>14.4f}" - ) + print(f"{j:<24} {r.mean():>10.4f} {g.mean():>10.4f} {r.std():>10.4f} {g.std():>10.4f} {kl:>14.4f}") result.update( { @@ -259,12 +229,7 @@ def validate_marginals( phys_gen = np.concatenate(all_phys_gen, axis=0) # (M, 2) if len(phys_real) > 0 and len(phys_gen) > 0: - phys_kl = np.array( - [ - _histogram_kl(phys_real[:, j], phys_gen[:, j], bins=kl_bins) - for j in range(2) - ] - ) + phys_kl = np.array([_histogram_kl(phys_real[:, j], phys_gen[:, j], bins=kl_bins) for j in range(2)]) else: phys_kl = np.full(2, np.nan) @@ -278,21 +243,14 @@ def validate_marginals( if len(r) == 0 or len(g) == 0: continue print( - f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} " - f"{r.std():>10.4f} {g.std():>10.4f} {phys_kl[j]:>14.4f}" + f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} {r.std():>10.4f} {g.std():>10.4f} {phys_kl[j]:>14.4f}" ) - result.update( - {"phys_real": phys_real, "phys_generated": phys_gen, "phys_kl": phys_kl} - ) + result.update({"phys_real": phys_real, "phys_generated": phys_gen, "phys_kl": phys_kl}) else: type_class_real = np.concatenate(all_type_class_real, axis=0) type_class_gen = np.concatenate(all_type_class_gen, axis=0) - n_classes = ( - sec_decoder.type_dim - if target == "onehot" - else sec_decoder.cond_enc.pdg_emb.weight.size(0) - ) + n_classes = sec_decoder.type_dim if target == "onehot" else sec_decoder.cond_enc.pdg_emb.weight.size(0) type_class_kl = _categorical_kl(type_class_real, type_class_gen, n_classes) print( diff --git a/pyproject.toml b/pyproject.toml index a8c08f3..1d7538b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,9 @@ analysis = [ giant = "giant.cli:app" dwarf = "scripts.dwarf:app" +[tool.ruff] +line-length = 120 + [tool.coverage.run] source = ["giant", "scripts"] omit = ["*/legacy/*"] diff --git a/scripts/bump_dataset_version.py b/scripts/bump_dataset_version.py index 216748a..27ecabe 100644 --- a/scripts/bump_dataset_version.py +++ b/scripts/bump_dataset_version.py @@ -82,9 +82,7 @@ def _max_index(parent: Path, pattern: re.Pattern) -> int: def _git_user_name() -> str | None: try: - out = subprocess.run( - ["git", "config", "user.name"], capture_output=True, text=True, timeout=2 - ) + out = subprocess.run(["git", "config", "user.name"], capture_output=True, text=True, timeout=2) except (OSError, subprocess.SubprocessError): # OSError (e.g. git not on PATH) and subprocess.SubprocessError # (e.g. TimeoutExpired) are unrelated hierarchies — TimeoutExpired @@ -142,9 +140,7 @@ def plan_bump_schema( raw_gen_dir = root / "raw" / kind / gen_tag processed_gen_dir = root / "processed" / kind / gen_tag if not raw_gen_dir.is_dir() and not processed_gen_dir.is_dir(): - raise SystemExit( - f"error: {gen_tag} doesn't exist yet for kind={kind} — run bump-gen first" - ) + raise SystemExit(f"error: {gen_tag} doesn't exist yet for kind={kind} — run bump-gen first") if target is not None: if not SCHEMA_RE.match(target): raise SystemExit(f"error: --to must look like 'schemaN', got {target!r}") @@ -154,9 +150,7 @@ def plan_bump_schema( schema_tag = f"schema{next_schema}" new_dirs = [processed_gen_dir / schema_tag] by_suffix = f" ({by})" if by else "" - log_line = ( - f"- `{gen_tag}`/`{schema_tag}` (kind={kind}) — {date} — {reason}{by_suffix}" - ) + log_line = f"- `{gen_tag}`/`{schema_tag}` (kind={kind}) — {date} — {reason}{by_suffix}" return new_dirs, log_line @@ -212,9 +206,7 @@ def _manifest_referenced_files(pools_root: Path) -> set[Path]: return referenced -def _referenced_root_count( - raw_gen_dir: Path, processed_gen_dir: Path -) -> tuple[int, int]: +def _referenced_root_count(raw_gen_dir: Path, processed_gen_dir: Path) -> tuple[int, int]: """(total .root files, count with a same-named .parquet under any schema) for one gen.""" if not raw_gen_dir.is_dir(): return 0, 0 @@ -243,9 +235,7 @@ def _referenced_root_count( return total, referenced -def _referenced_parquet_count( - schema_dir: Path, manifest_referenced: set[Path] -) -> tuple[int, int]: +def _referenced_parquet_count(schema_dir: Path, manifest_referenced: set[Path]) -> tuple[int, int]: """(total .parquet files, count listed in at least one manifest) for one schema dir.""" if not schema_dir.is_dir(): return 0, 0 @@ -342,11 +332,7 @@ def print_status(root: Path) -> None: grand_files = 0 for kind_dir in sorted(p for p in raw_root.iterdir() if p.is_dir()): kind = kind_dir.name - gens = sorted( - int(m.group(1)) - for m in (GEN_RE.match(p.name) for p in kind_dir.iterdir() if p.is_dir()) - if m - ) + gens = sorted(int(m.group(1)) for m in (GEN_RE.match(p.name) for p in kind_dir.iterdir() if p.is_dir()) if m) print(_colorize(f"{kind}/", "kind")) kind_total = 0 kind_files = 0 @@ -359,25 +345,18 @@ def print_status(root: Path) -> None: schemas = sorted( int(m.group(1)) for m in ( - SCHEMA_RE.match(p.name) - for p in (schema_dir.iterdir() if schema_dir.is_dir() else []) - if p.is_dir() + SCHEMA_RE.match(p.name) for p in (schema_dir.iterdir() if schema_dir.is_dir() else []) if p.is_dir() ) if m ) schema_sizes = {s: _du(schema_dir / f"schema{s}") for s in schemas} schema_counts = { - s: _referenced_parquet_count( - schema_dir / f"schema{s}", manifest_referenced - ) - for s in schemas + s: _referenced_parquet_count(schema_dir / f"schema{s}", manifest_referenced) for s in schemas } processed_size = sum(schema_sizes.values()) processed_files = sum(c[0] for c in schema_counts.values()) processed_referenced = sum(c[1] for c in schema_counts.values()) - raw_files, raw_referenced = _referenced_root_count( - raw_gen_dir, processed_gen_dir - ) + raw_files, raw_referenced = _referenced_root_count(raw_gen_dir, processed_gen_dir) gen_total = raw_size + processed_size gen_files = raw_files + processed_files kind_total += gen_total @@ -425,9 +404,7 @@ def print_status(root: Path) -> None: print(_reason_line(schema_reason, indent=4)) else: print(_colorize(" (none)", "schema")) - print( - _row(f"{kind} total", kind_total, indent=1, level="gen", count=kind_files) - ) + print(_row(f"{kind} total", kind_total, indent=1, level="gen", count=kind_files)) print() grand_total += kind_total grand_files += kind_files @@ -526,13 +503,8 @@ def plan_update_manifest( return result, missing -def apply_update_manifest( - manifest_path: Path, lines: list[tuple[str, str | None]] -) -> None: - out = [ - replacement if replacement is not None else original - for original, replacement in lines - ] +def apply_update_manifest(manifest_path: Path, lines: list[tuple[str, str | None]]) -> None: + out = [replacement if replacement is not None else original for original, replacement in lines] manifest_path.write_text("\n".join(out) + "\n") @@ -552,9 +524,7 @@ def _resolve_manifest_files(manifest_path: Path) -> list[Path]: return files -def plan_create_manifest( - output_path: Path, parquet_files: list[Path] -) -> tuple[list[str], list[Path], list[Path]]: +def plan_create_manifest(output_path: Path, parquet_files: list[Path]) -> tuple[list[str], list[Path], list[Path]]: """Return (relative_lines, missing_files, resolved_abs_paths).""" manifest_dir = output_path.resolve().parent lines: list[str] = [] @@ -569,9 +539,7 @@ def plan_create_manifest( return lines, missing, resolved -def check_holdout_overlap( - output_path: Path, resolved_new_files: list[Path] -) -> list[tuple[str, Path]]: +def check_holdout_overlap(output_path: Path, resolved_new_files: list[Path]) -> list[tuple[str, Path]]: """Return (other_manifest_name, file) pairs where new files clash with existing manifests. The check is triggered when output_path is (or will be) holdout.manifest, or when a @@ -641,9 +609,7 @@ def _run_bump( if gen is None: new_dirs, log_line = plan_bump_gen(root_path, kind, reason, by, date, to) else: - new_dirs, log_line = plan_bump_schema( - root_path, kind, gen, reason, by, date, to - ) + new_dirs, log_line = plan_bump_schema(root_path, kind, gen, reason, by, date, to) print(f"=== {'EXECUTING' if execute else 'DRY RUN'} ===") print("new directories:") diff --git a/scripts/check_migration_v02_v03.py b/scripts/check_migration_v02_v03.py index 3136d11..3386a91 100644 --- a/scripts/check_migration_v02_v03.py +++ b/scripts/check_migration_v02_v03.py @@ -84,19 +84,12 @@ def main() -> int: mode = model_config.get("mode", "flow") routed = bool((model_config.get("router") or {}).get("enabled")) print(f"checkpoint: {args.checkpoint}") - print( - f" mode={mode!r} conditioning={model_config.get('conditioning')!r} " - f"routed={routed} ema={args.ema}" - ) + print(f" mode={mode!r} conditioning={model_config.get('conditioning')!r} routed={routed} ema={args.ema}") stage1_key = "model_ema" if args.ema and "model_ema" in ckpt else "model" - stage2_key = ( - "sec_decoder_ema" if args.ema and "sec_decoder_ema" in ckpt else "sec_decoder" - ) + stage2_key = "sec_decoder_ema" if args.ema and "sec_decoder_ema" in ckpt else "sec_decoder" if args.ema and stage1_key == "model": - print( - " warning: --ema requested but no model_ema in checkpoint, using raw weights" - ) + print(" warning: --ema requested but no model_ema in checkpoint, using raw weights") # --- old side: the frozen v0.2 snapshot, loaded with the checkpoint's own weights --- old_stage1, old_stage2 = legacy.build_models(model_config) @@ -119,9 +112,7 @@ def main() -> int: print("PASS (construction only, routed checkpoint)") return 0 - remapped1, remapped2 = net.migrate_legacy_state_dict( - ckpt[stage1_key], ckpt[stage2_key] - ) + remapped1, remapped2 = net.migrate_legacy_state_dict(ckpt[stage1_key], ckpt[stage2_key]) missing1, unexpected1 = new_stage1.load_state_dict(remapped1, strict=True) missing2, unexpected2 = new_stage2.load_state_dict(remapped2, strict=True) if missing1 or unexpected1 or missing2 or unexpected2: @@ -132,9 +123,7 @@ def main() -> int: new_stage1.eval() new_stage2.eval() - cond_cont, cond_cat, x1, x2, t, z1, z2 = _random_batch( - model_config, args.batch, args.seed - ) + cond_cont, cond_cat, x1, x2, t, z1, z2 = _random_batch(model_config, args.batch, args.seed) ok = True with torch.no_grad(): diff --git a/scripts/create_root_files.py b/scripts/create_root_files.py index d960c78..66ba11a 100644 --- a/scripts/create_root_files.py +++ b/scripts/create_root_files.py @@ -64,9 +64,7 @@ def parse_detector_spec(spec: str) -> tuple[str, str | None]: if ":" in spec: label, config = spec.split(":", 1) if not label or not config: - raise PlanError( - f"invalid --detector spec {spec!r}: expected NAME or NAME:CONFIG" - ) + raise PlanError(f"invalid --detector spec {spec!r}: expected NAME or NAME:CONFIG") return label, config return spec, None @@ -94,9 +92,7 @@ def plan_jobs( raise PlanError(f"--gen must look like 'genN', got {gen!r}") gen_dir = dataset_root / "raw" / kind / gen if not gen_dir.is_dir(): - raise PlanError( - f"{gen_dir} doesn't exist — run bump_dataset_version.py bump-gen first" - ) + raise PlanError(f"{gen_dir} doesn't exist — run bump_dataset_version.py bump-gen first") jobs = [] for spec in detector_specs: @@ -124,9 +120,7 @@ def job_seed(kind: str, gen: str, job: SimJob, energy_gev: float | None) -> int: return zlib.crc32(key.encode()) & 0x7FFFFFFF -def build_cmd( - executable: Path, job: SimJob, events_per_file: int, energy_gev: float | None -) -> list[str]: +def build_cmd(executable: Path, job: SimJob, events_per_file: int, energy_gev: float | None) -> list[str]: """minicalosim executables take positional `[configName] nEvents [energy_GeV]`.""" cmd = [str(executable)] if job.config: @@ -147,10 +141,7 @@ def run_job( gen: str, tmp_root: Path, ) -> JobResult: - workdir = ( - tmp_root - / f"{kind}-{gen}-{job.detector}-{job.shard_index:03d}-{uuid.uuid4().hex[:8]}" - ) + workdir = tmp_root / f"{kind}-{gen}-{job.detector}-{job.shard_index:03d}-{uuid.uuid4().hex[:8]}" workdir.mkdir(parents=True) cmd = build_cmd(executable, job, events_per_file, energy_gev) @@ -174,20 +165,12 @@ def run_job( job, False, None, - f"expected exactly one .root output in {workdir}, found {len(produced)}: " - f"{[p.name for p in produced]}", + f"expected exactly one .root output in {workdir}, found {len(produced)}: {[p.name for p in produced]}", result.stdout, result.stderr, ) - dest = ( - dataset_root - / "raw" - / kind - / gen - / job.detector - / f"shard-{job.shard_index:03d}.root" - ) + dest = dataset_root / "raw" / kind / gen / job.detector / f"shard-{job.shard_index:03d}.root" if dest.exists(): return JobResult( job, @@ -279,14 +262,7 @@ def run_make_root( print(f"executable: {executable}") for job in planned_jobs: cmd = build_cmd(executable, job, events_per_file, energy_gev) - dest = ( - dataset_root_path - / "raw" - / kind - / gen - / job.detector - / f"shard-{job.shard_index:03d}.root" - ) + dest = dataset_root_path / "raw" / kind / gen / job.detector / f"shard-{job.shard_index:03d}.root" seed = job_seed(kind, gen, job, energy_gev) print(f" MINICALOSIM_SEED={seed} {' '.join(cmd)} -> {dest}") diff --git a/scripts/dwarf.py b/scripts/dwarf.py index 5e7cff5..674e252 100644 --- a/scripts/dwarf.py +++ b/scripts/dwarf.py @@ -80,8 +80,7 @@ def convert( typer.Option( "--output", "-o", - help="Output Parquet file (default: .parquet). Only valid " - "with a single input file and --jobs 1.", + help="Output Parquet file (default: .parquet). Only valid with a single input file and --jobs 1.", ), ] = None, batch_size: Annotated[ @@ -91,9 +90,7 @@ def convert( help="Uproot read batch size, e.g. '100 MB' or '500000' (rows)", ), ] = "100 MB", - tree: Annotated[ - str, typer.Option("--tree", help="Tree name inside the ROOT file") - ] = "Steps", + tree: Annotated[str, typer.Option("--tree", help="Tree name inside the ROOT file")] = "Steps", compression: Annotated[ Compression, typer.Option("--compression", help="Parquet compression codec") ] = Compression.snappy, @@ -129,15 +126,11 @@ def convert( raise typer.Exit(1) _warn_if_exceeds_shared_quota(jobs, "--jobs") - compression_value = ( - "uncompressed" if compression is Compression.none else compression.value - ) + compression_value = "uncompressed" if compression is Compression.none else compression.value if jobs == 1: if output is not None and len(root_files) > 1: - typer.echo( - "error: --output can only be used with a single input file", err=True - ) + typer.echo("error: --output can only be used with a single input file", err=True) raise typer.Exit(1) total_orphaned = 0 for root_file in root_files: @@ -150,10 +143,7 @@ def convert( ) total_orphaned += n_orphaned if total_orphaned: - typer.echo( - f"\n{total_orphaned} orphaned child track(s) dropped across " - f"{len(root_files)} file(s)." - ) + typer.echo(f"\n{total_orphaned} orphaned child track(s) dropped across {len(root_files)} file(s).") return if output is not None: @@ -176,9 +166,7 @@ def convert( @app.command() def migrate( - root: Annotated[ - Path, typer.Argument(help="Dataset root to migrate in place") - ] = _DATASET_ROOT_DEFAULT, + root: Annotated[Path, typer.Argument(help="Dataset root to migrate in place")] = _DATASET_ROOT_DEFAULT, execute: Annotated[ bool, typer.Option( @@ -190,8 +178,7 @@ def migrate( bool, typer.Option( "--copy", - help="Copy instead of move, leaving the originals in place " - "(e.g. if another process is still reading them)", + help="Copy instead of move, leaving the originals in place (e.g. if another process is still reading them)", ), ] = False, ) -> None: @@ -202,12 +189,8 @@ def migrate( @app.command("bump-gen") def bump_gen( reason: Annotated[str, typer.Option("--reason", help="Why this gen exists")], - kind: Annotated[ - str, typer.Option("--kind", help="steps | hits | ... (default: steps)") - ] = "steps", - by: Annotated[ - Optional[str], typer.Option("--by", help="Attribution (default: git user.name)") - ] = None, + kind: Annotated[str, typer.Option("--kind", help="steps | hits | ... (default: steps)")] = "steps", + by: Annotated[Optional[str], typer.Option("--by", help="Attribution (default: git user.name)")] = None, date: Annotated[ Optional[str], typer.Option("--date", help="Override date (default: today, ISO)"), @@ -220,12 +203,8 @@ def bump_gen( help="Target gen tag (default: one past the current highest)", ), ] = None, - execute: Annotated[ - bool, typer.Option("--execute", help="Apply (default: dry run)") - ] = False, - root: Annotated[ - Path, typer.Option("--root", help="Dataset root") - ] = _DATASET_ROOT_DEFAULT, + execute: Annotated[bool, typer.Option("--execute", help="Apply (default: dry run)")] = False, + root: Annotated[Path, typer.Option("--root", help="Dataset root")] = _DATASET_ROOT_DEFAULT, ) -> None: """Cut a new raw generation.""" run_bump_gen( @@ -243,12 +222,8 @@ def bump_gen( def bump_schema( gen: Annotated[str, typer.Option("--gen", help="Existing gen tag, e.g. gen1")], reason: Annotated[str, typer.Option("--reason", help="Why this schema exists")], - kind: Annotated[ - str, typer.Option("--kind", help="steps | hits | ... (default: steps)") - ] = "steps", - by: Annotated[ - Optional[str], typer.Option("--by", help="Attribution (default: git user.name)") - ] = None, + kind: Annotated[str, typer.Option("--kind", help="steps | hits | ... (default: steps)")] = "steps", + by: Annotated[Optional[str], typer.Option("--by", help="Attribution (default: git user.name)")] = None, date: Annotated[ Optional[str], typer.Option("--date", help="Override date (default: today, ISO)"), @@ -261,12 +236,8 @@ def bump_schema( help="Target schema tag (default: one past the current highest)", ), ] = None, - execute: Annotated[ - bool, typer.Option("--execute", help="Apply (default: dry run)") - ] = False, - root: Annotated[ - Path, typer.Option("--root", help="Dataset root") - ] = _DATASET_ROOT_DEFAULT, + execute: Annotated[bool, typer.Option("--execute", help="Apply (default: dry run)")] = False, + root: Annotated[Path, typer.Option("--root", help="Dataset root")] = _DATASET_ROOT_DEFAULT, ) -> None: """Cut a new schema within a gen.""" run_bump_schema( @@ -283,9 +254,7 @@ def bump_schema( @app.command() def status( - root: Annotated[ - Path, typer.Option("--root", help="Dataset root") - ] = _DATASET_ROOT_DEFAULT, + root: Annotated[Path, typer.Option("--root", help="Dataset root")] = _DATASET_ROOT_DEFAULT, ) -> None: """List existing gens/schemas per kind.""" run_status(str(root)) @@ -293,9 +262,7 @@ def status( @app.command("update-manifest") def update_manifest( - manifests: Annotated[ - list[Path], typer.Argument(help="One or more .manifest files to update") - ], + manifests: Annotated[list[Path], typer.Argument(help="One or more .manifest files to update")], schema: Annotated[ Optional[str], typer.Option( @@ -306,9 +273,7 @@ def update_manifest( ] = None, gen: Annotated[ Optional[str], - typer.Option( - "--gen", metavar="genN", help="Target gen tag (default: keep existing gen)" - ), + typer.Option("--gen", metavar="genN", help="Target gen tag (default: keep existing gen)"), ] = None, execute: Annotated[ bool, @@ -316,9 +281,7 @@ def update_manifest( ] = False, ) -> None: """Repoint manifest(s) to a new gen and/or schema, verifying all target files exist.""" - run_update_manifest( - [str(m) for m in manifests], schema=schema, execute=execute, gen=gen - ) + run_update_manifest([str(m) for m in manifests], schema=schema, execute=execute, gen=gen) @app.command("create-manifest") @@ -333,22 +296,15 @@ def create_manifest( typer.Option( "--pool", metavar="DETECTOR", - help="Detector name; combined with --type and --root to form " - "/pools//.manifest", + help="Detector name; combined with --type and --root to form /pools//.manifest", ), ] = None, type_: Annotated[ Optional[PoolType], - typer.Option( - "--type", help="Pool type — full, holdout, or dev (required with --pool)" - ), + typer.Option("--type", help="Pool type — full, holdout, or dev (required with --pool)"), ] = None, - root: Annotated[ - Path, typer.Option("--root", help="Dataset root (used with --pool)") - ] = _DATASET_ROOT_DEFAULT, - execute: Annotated[ - bool, typer.Option("--execute", help="Write the manifest (default: dry run)") - ] = False, + root: Annotated[Path, typer.Option("--root", help="Dataset root (used with --pool)")] = _DATASET_ROOT_DEFAULT, + execute: Annotated[bool, typer.Option("--execute", help="Write the manifest (default: dry run)")] = False, force: Annotated[ bool, typer.Option("--force", help="Overwrite the manifest if it already exists"), @@ -368,9 +324,7 @@ def create_manifest( @app.command("make-root") def make_root( - executable: Annotated[ - Path, typer.Option("--executable", help="Built minicalosim run_* executable") - ], + executable: Annotated[Path, typer.Option("--executable", help="Built minicalosim run_* executable")], detector: Annotated[ list[str], typer.Option( @@ -382,15 +336,9 @@ def make_root( "Repeatable.", ), ], - num_files: Annotated[ - int, typer.Option("--num-files", help="New shards to create per detector") - ], - events_per_file: Annotated[ - int, typer.Option("--events-per-file", help="nEvents passed to the executable") - ], - gen: Annotated[ - str, typer.Option("--gen", help="Existing gen tag under raw//, e.g. gen1") - ], + num_files: Annotated[int, typer.Option("--num-files", help="New shards to create per detector")], + events_per_file: Annotated[int, typer.Option("--events-per-file", help="nEvents passed to the executable")], + gen: Annotated[str, typer.Option("--gen", help="Existing gen tag under raw//, e.g. gen1")], energy_gev: Annotated[ float | None, typer.Option( @@ -401,20 +349,12 @@ def make_root( "to name the dataset accordingly.", ), ] = None, - kind: Annotated[ - str, typer.Option("--kind", help="steps | hits | ... (default: steps)") - ] = "steps", - dataset_root: Annotated[ - Path, typer.Option("--dataset-root", help="Dataset root") - ] = _DATASET_ROOT_DEFAULT, - jobs: Annotated[ - int, typer.Option("--jobs", "-j", help="Parallel simulation runs (default: 4)") - ] = 4, + kind: Annotated[str, typer.Option("--kind", help="steps | hits | ... (default: steps)")] = "steps", + dataset_root: Annotated[Path, typer.Option("--dataset-root", help="Dataset root")] = _DATASET_ROOT_DEFAULT, + jobs: Annotated[int, typer.Option("--jobs", "-j", help="Parallel simulation runs (default: 4)")] = 4, execute: Annotated[ bool, - typer.Option( - "--execute", help="Actually run jobs (default: dry run / print plan)" - ), + typer.Option("--execute", help="Actually run jobs (default: dry run / print plan)"), ] = False, ) -> None: """Generate new ROOT shards via a minicalosim executable.""" @@ -441,9 +381,7 @@ class OracleMethod(str, Enum): @app.command("build-geometry-oracle") def build_geometry_oracle( - data: Annotated[ - Path, typer.Argument(help="Steps parquet file or directory of steps files") - ], + data: Annotated[Path, typer.Argument(help="Steps parquet file or directory of steps files")], out: Annotated[Path, typer.Option("--out", "-o", help="Output oracle .pkl path")], method: Annotated[ OracleMethod, @@ -456,9 +394,7 @@ def build_geometry_oracle( ), ), ] = OracleMethod.slab, - k: Annotated[ - int, typer.Option("--k", help="Neighbours for the knn classifier") - ] = 1, + k: Annotated[int, typer.Option("--k", help="Neighbours for the knn classifier")] = 1, subsample: Annotated[ int, typer.Option("--subsample", help="Max reference points sampled from the data"), @@ -504,9 +440,7 @@ def build_geometry_oracle( def warm_cache( data: Annotated[ Path, - typer.Argument( - help="Parquet file, directory, or .manifest — same as `giant train`'s" - ), + typer.Argument(help="Parquet file, directory, or .manifest — same as `giant train`'s"), ], val_fraction: Annotated[ float, @@ -518,16 +452,13 @@ def warm_cache( ] = 0.1, seed: Annotated[ int, - typer.Option( - "--seed", "-s", help="Must match the `giant train` run(s) to warm for" - ), + typer.Option("--seed", "-s", help="Must match the `giant train` run(s) to warm for"), ] = 0, particle_conditioning: Annotated[ Conditioning, typer.Option( "--particle-conditioning", - help="Must match the `giant train` run(s)' conditioning.particle.type " - "to warm for", + help="Must match the `giant train` run(s)' conditioning.particle.type to warm for", ), ] = Conditioning.physical, material_conditioning: Annotated[ @@ -543,21 +474,14 @@ def warm_cache( bool, typer.Option( "--router/--no-router", - help="Warm the process vocabulary too (only takes effect with " - "--router-type process)", + help="Warm the process vocabulary too (only takes effect with --router-type process)", ), ] = False, - router_type: Annotated[ - str, typer.Option("--router-type", help="Router implementation name") - ] = "energy", - n_experts: Annotated[ - int, typer.Option("--n-experts", help="Number of routed experts") - ] = 4, + router_type: Annotated[str, typer.Option("--router-type", help="Router implementation name")] = "energy", + n_experts: Annotated[int, typer.Option("--n-experts", help="Number of routed experts")] = 4, rebuild: Annotated[ bool, - typer.Option( - "--rebuild", help="Ignore any existing sidecar and recompute every section" - ), + typer.Option("--rebuild", help="Ignore any existing sidecar and recompute every section"), ] = False, ) -> None: """Precompute `giant train`'s setup-stage sidecar for `data` ahead of time. diff --git a/scripts/geometry_oracle.py b/scripts/geometry_oracle.py index e5a36fb..762d288 100644 --- a/scripts/geometry_oracle.py +++ b/scripts/geometry_oracle.py @@ -38,9 +38,7 @@ def run_build_geometry_oracle( n_bins=n_bins, ) - print( - f"method: {method} reference points: {oracle.metadata['n_reference_points']:,}" - ) + print(f"method: {method} reference points: {oracle.metadata['n_reference_points']:,}") print("classes (material, layer_id):") for material, layer_id in oracle.classes: print(f" {material:<12} layer_id={layer_id}") diff --git a/scripts/hparam_scan.py b/scripts/hparam_scan.py index 00efd42..0c64f6c 100644 --- a/scripts/hparam_scan.py +++ b/scripts/hparam_scan.py @@ -154,9 +154,7 @@ def run_hparam_scan( wall_time_s = time.monotonic() - start if metrics_path.exists(): - epochs_completed, final_val_loss, best_val_loss = final_metrics( - metrics_path - ) + epochs_completed, final_val_loss, best_val_loss = final_metrics(metrics_path) append_summary( summary_path, { @@ -171,11 +169,6 @@ def run_hparam_scan( "wall_time_s": round(wall_time_s, 1), }, ) - print( - f"[{i}/{len(runs)}] {name} — val_loss {final_val_loss:.4f} " - f"({wall_time_s:.1f}s)" - ) + print(f"[{i}/{len(runs)}] {name} — val_loss {final_val_loss:.4f} ({wall_time_s:.1f}s)") else: - print( - f"[{i}/{len(runs)}] {name} — no metrics.csv produced, check train.log" - ) + print(f"[{i}/{len(runs)}] {name} — no metrics.csv produced, check train.log") diff --git a/scripts/migrate_geant_steps.py b/scripts/migrate_geant_steps.py index 27e96c9..447e0dc 100644 --- a/scripts/migrate_geant_steps.py +++ b/scripts/migrate_geant_steps.py @@ -50,12 +50,8 @@ PREDICTED_RE = re.compile( r"^(?P[a-z0-9]+(?:_[a-z0-9]+)*)_10k_(?P\d+)" r"_predicted(?P_local)?\.parquet$" ) -SHARD_RE = re.compile( - r"^(?P[a-z0-9]+(?:_[a-z0-9]+)*)_10k_(?P\d+)\.(?Proot|parquet)$" -) -LEGACY_PREDICTED_RE = re.compile( - r"^pbwo4_10000events_hits_predicted(?P_local)?\.parquet$" -) +SHARD_RE = re.compile(r"^(?P[a-z0-9]+(?:_[a-z0-9]+)*)_10k_(?P\d+)\.(?Proot|parquet)$") +LEGACY_PREDICTED_RE = re.compile(r"^pbwo4_10000events_hits_predicted(?P_local)?\.parquet$") LEGACY_RE = re.compile(r"^pbwo4_10000events_hits\.(?Proot|parquet)$") @@ -110,24 +106,9 @@ def plan_moves(src_root: Path) -> tuple[list[tuple[Path, Path]], list[Path]]: if m: detector, shard, ext = m["detector"], int(m["shard"]), m["ext"] if ext == "root": - dst = ( - src_root - / "raw" - / "steps" - / GEN - / detector - / f"shard-{shard:03d}.root" - ) + dst = src_root / "raw" / "steps" / GEN / detector / f"shard-{shard:03d}.root" else: - dst = ( - src_root - / "processed" - / "steps" - / GEN - / SCHEMA - / detector - / f"shard-{shard:03d}.parquet" - ) + dst = src_root / "processed" / "steps" / GEN / SCHEMA / detector / f"shard-{shard:03d}.parquet" moves.append((path, dst)) continue @@ -135,19 +116,9 @@ def plan_moves(src_root: Path) -> tuple[list[tuple[Path, Path]], list[Path]]: if m: ext = m["ext"] if ext == "root": - dst = ( - src_root / "raw" / "hits" / LEGACY_GEN / "pbwo4" / "shard-000.root" - ) + dst = src_root / "raw" / "hits" / LEGACY_GEN / "pbwo4" / "shard-000.root" else: - dst = ( - src_root - / "processed" - / "hits" - / LEGACY_GEN - / LEGACY_SCHEMA - / "pbwo4" - / "shard-000.parquet" - ) + dst = src_root / "processed" / "hits" / LEGACY_GEN / LEGACY_SCHEMA / "pbwo4" / "shard-000.parquet" moves.append((path, dst)) continue @@ -165,20 +136,9 @@ def plan_manifests(src_root: Path) -> dict[Path, list[str]]: for pool, shards in rules.items(): manifest_path = manifest_dir / f"{pool}{MANIFEST_SUFFIX}" for shard in shards: - dst = ( - src_root - / "processed" - / "steps" - / GEN - / SCHEMA - / detector - / f"shard-{shard:03d}.parquet" - ) + dst = src_root / "processed" / "steps" / GEN / SCHEMA / detector / f"shard-{shard:03d}.parquet" manifests[manifest_path].append((shard, dst)) - return { - k: [os.path.relpath(dst, start=k.parent) for _, dst in sorted(v)] - for k, v in manifests.items() - } + return {k: [os.path.relpath(dst, start=k.parent) for _, dst in sorted(v)] for k, v in manifests.items()} def run_migration(root: str, execute: bool, copy: bool) -> None: diff --git a/scripts/profile_analysis_costs.py b/scripts/profile_analysis_costs.py index de97a2b..60dda89 100644 --- a/scripts/profile_analysis_costs.py +++ b/scripts/profile_analysis_costs.py @@ -94,9 +94,7 @@ def _make_rollout(n: int, n_events: int, seed: int) -> pl.DataFrame: "post_dx": post_dir[:, 0], "post_dy": post_dir[:, 1], "post_dz": post_dir[:, 2], - "edep": np.where( - is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep - ), + "edep": np.where(is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep), "step_length": np.where(is_synthetic, 0.0, step_length), "material": rng.choice(_MATERIALS, size=n), "layer_id": rng.integers(0, 30, size=n), @@ -163,9 +161,7 @@ def _make_reference(n: int, n_events: int, seed: int) -> pl.DataFrame: ) -def _time( - spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path -) -> float: +def _time(spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path) -> float: t0 = time.perf_counter() compute_reduced( spec_id, diff --git a/scripts/steps_to_parquet_parallel.py b/scripts/steps_to_parquet_parallel.py index b2ee41e..1817c36 100644 --- a/scripts/steps_to_parquet_parallel.py +++ b/scripts/steps_to_parquet_parallel.py @@ -49,9 +49,7 @@ def latest_schema_tag(processed_gen_dir: Path) -> str | None: return best_tag -def resolve_destination( - root_file: Path, dataset_root: Path, schema_override: str | None -) -> Path: +def resolve_destination(root_file: Path, dataset_root: Path, schema_override: str | None) -> Path: """Map raw////.root (relative to *dataset_root*) to processed/////.parquet. @@ -66,12 +64,7 @@ def resolve_destination( raise DestinationError(f"{root_file} is not under dataset root {dataset_root}") parts = rel.parts - if ( - len(parts) != 5 - or parts[0] != "raw" - or not GEN_RE.match(parts[2]) - or not parts[4].endswith(".root") - ): + if len(parts) != 5 or parts[0] != "raw" or not GEN_RE.match(parts[2]) or not parts[4].endswith(".root"): raise DestinationError( f"{root_file} does not match raw////.root " f"under {dataset_root} (got relative path: {rel})" @@ -216,13 +209,7 @@ def run_parallel_job( print(f" {root_file}", file=sys.stderr) raise SystemExit(1) - total_orphaned = sum( - int(m.group(1)) - for _, _, stdout, _ in results - for m in _ORPHAN_RE.finditer(stdout) - ) + total_orphaned = sum(int(m.group(1)) for _, _, stdout, _ in results for m in _ORPHAN_RE.finditer(stdout)) if total_orphaned: - print( - f"\n{total_orphaned} orphaned child track(s) dropped across {len(results)} file(s)." - ) + print(f"\n{total_orphaned} orphaned child track(s) dropped across {len(results)} file(s).") print(f"\nAll {len(results)} conversion(s) completed.") diff --git a/tests/legacy/network_v02_snapshot.py b/tests/legacy/network_v02_snapshot.py index ea9364c..a58992d 100644 --- a/tests/legacy/network_v02_snapshot.py +++ b/tests/legacy/network_v02_snapshot.py @@ -37,11 +37,7 @@ class SinusoidalEmbedding(nn.Module): super().__init__() assert dim % 2 == 0, "dim must be even" half = dim // 2 - freqs = torch.exp( - -math.log(10000) - * torch.arange(half, dtype=torch.float32) - / max(half - 1, 1) - ) + freqs = torch.exp(-math.log(10000) * torch.arange(half, dtype=torch.float32) / max(half - 1, 1)) self.register_buffer("freqs", freqs) def forward(self, t: torch.Tensor) -> torch.Tensor: @@ -107,9 +103,7 @@ class ConditionEncoder(nn.Module): pdg_e = self.pdg_emb(cond_cat[:, 0]) mat_e = self.mat_emb(cond_cat[:, 1]) else: - particle_phys = cond_cont[ - :, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM - ] + particle_phys = cond_cont[:, COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM] material_phys = cond_cont[:, COND_DIM_BASE + PARTICLE_PHYS_DIM :] pdg_e = self.particle_mlp(particle_phys) mat_e = self.material_mlp(material_phys) @@ -168,12 +162,7 @@ class DenoisingMLP(nn.Module): ) merged_cond_dim = time_dim + cond_out_dim self.input_proj = nn.Linear(x_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, x_dim) # Predicts n_sec as classification over {0, 1, ..., k_max}. # Applied to the condition encoding (not the diffused latent). @@ -286,12 +275,7 @@ class SecondaryDecoder(nn.Module): ) merged_cond_dim = time_dim + cond_out_dim self.input_proj = nn.Linear(sec_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, sec_dim) def forward( @@ -345,12 +329,7 @@ class WGANGenerator(nn.Module): conditioning=conditioning, ) self.input_proj = nn.Linear(noise_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, x_dim) self.n_sec_head = nn.Sequential( nn.Linear(cond_out_dim, hidden_dim // 2), @@ -410,12 +389,7 @@ class Critic(nn.Module): conditioning=conditioning, ) self.input_proj = nn.Linear(x_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_norm = nn.LayerNorm(hidden_dim) self.out_proj = nn.Linear(hidden_dim, 1) @@ -466,12 +440,7 @@ class WGANSecondaryGenerator(nn.Module): conditioning=conditioning, ) self.input_proj = nn.Linear(noise_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, sec_dim) def forward( @@ -515,12 +484,7 @@ class SecondaryCritic(nn.Module): conditioning=conditioning, ) self.input_proj = nn.Linear(sec_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, cond_out_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_norm = nn.LayerNorm(hidden_dim) self.out_proj = nn.Linear(hidden_dim, 1) @@ -550,9 +514,7 @@ class Router(nn.Module): def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: raise NotImplementedError - def combine_weights( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def combine_weights(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: probs = self.gate(cond_cont, cond_cat) if not (self.gumbel and self.training): return probs @@ -562,26 +524,18 @@ class Router(nn.Module): def top1(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: return self.gate(cond_cont, cond_cat).argmax(dim=-1) - def balance_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def balance_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: importance = self.gate(cond_cont, cond_cat).sum(dim=0) # (n_experts,) return (importance.std() / (importance.mean() + 1e-8)) ** 2 - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: return torch.zeros((), device=cond_cont.device) - def entropy_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> torch.Tensor: + def entropy_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: norm_entropy, _ = self.gate_stats(cond_cont, cond_cat) return norm_entropy - def gate_stats( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor - ) -> tuple[torch.Tensor, torch.Tensor]: + def gate_stats(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: gate = self.gate(cond_cont, cond_cat) # (B, n_experts) row_entropy = -(gate * (gate + 1e-8).log()).sum(dim=-1) # (B,) norm_entropy = row_entropy.mean() / math.log(self.n_experts) @@ -602,9 +556,7 @@ def register_router(name: str): def build_router(name: str, n_experts: int, **kwargs) -> Router: if name not in ROUTER_REGISTRY: - raise ValueError( - f"unknown router type {name!r}; available: {sorted(ROUTER_REGISTRY)}" - ) + raise ValueError(f"unknown router type {name!r}; available: {sorted(ROUTER_REGISTRY)}") cls = ROUTER_REGISTRY[name] accepted = set(inspect.signature(cls.__init__).parameters) - {"self", "n_experts"} filtered = {k: v for k, v in kwargs.items() if k in accepted} @@ -644,8 +596,7 @@ class EnergyRouter(Router): if learn_width or learn_temperature: if not (width_min_ratio < 1.0 < width_max_ratio): raise ValueError( - f"width_min_ratio ({width_min_ratio}) and width_max_ratio " - f"({width_max_ratio}) must bracket 1.0" + f"width_min_ratio ({width_min_ratio}) and width_max_ratio ({width_max_ratio}) must bracket 1.0" ) self._width_lo = width_min_ratio * temperature self._width_hi = width_max_ratio * temperature @@ -658,10 +609,7 @@ class EnergyRouter(Router): centers = torch.linspace(-2.0, 2.0, n_experts) else: if len(centers_init) != n_experts: - raise ValueError( - f"centers_init has {len(centers_init)} values, " - f"expected n_experts={n_experts}" - ) + raise ValueError(f"centers_init has {len(centers_init)} values, expected n_experts={n_experts}") centers = torch.tensor(list(centers_init), dtype=torch.float32) if learn_centers: self.centers = nn.Parameter(centers) @@ -702,9 +650,7 @@ class PdgRouter(Router): def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: e = self.pdg_emb(cond_cat[:, 0]) # (B, emb_dim) - d2 = ((e.unsqueeze(1) - self.centers.unsqueeze(0)) ** 2).sum( - -1 - ) # (B, n_experts) + d2 = ((e.unsqueeze(1) - self.centers.unsqueeze(0)) ** 2).sum(-1) # (B, n_experts) return torch.softmax(-d2 / self.temperature, dim=-1) @@ -736,9 +682,7 @@ class ProcessRouter(Router): def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor: return torch.softmax(self.logits(cond_cont, cond_cat), dim=-1) - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: return F.cross_entropy(self.logits(cond_cont, cond_cat), labels) @@ -756,14 +700,10 @@ class ComposedRouter(Router): joint = self.routers[0].gate(cond_cont, cond_cat) # (B, n_0) for router in self.routers[1:]: g = router.gate(cond_cont, cond_cat) # (B, n_i) - joint = (joint.unsqueeze(-1) * g.unsqueeze(1)).flatten( - 1 - ) # (B, prod so far) + joint = (joint.unsqueeze(-1) * g.unsqueeze(1)).flatten(1) # (B, prod so far) return joint - def classify_loss( - self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor - ) -> torch.Tensor: + def classify_loss(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: total = torch.zeros((), device=cond_cont.device) for router in self.routers: total = total + router.classify_loss(cond_cont, cond_cat, labels) @@ -796,12 +736,7 @@ class ExpertTrunk(nn.Module): ) -> None: super().__init__() self.input_proj = nn.Linear(in_dim, hidden_dim) - self.blocks = nn.ModuleList( - [ - ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) - for _ in range(n_blocks) - ] - ) + self.blocks = nn.ModuleList([ResBlock(hidden_dim, merged_cond_dim, dropout=dropout) for _ in range(n_blocks)]) self.out_proj = nn.Linear(hidden_dim, in_dim) def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: @@ -891,9 +826,7 @@ class RoutedDenoisingMLP(nn.Module): t_emb = self.time_emb(t) c_emb = self.cond_enc(cond_cont, cond_cat) cond = torch.cat([t_emb, c_emb], dim=-1) - return _route_forward( - self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training - ) + return _route_forward(self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training) def predict_n_sec( self, @@ -957,9 +890,7 @@ class RoutedSecondaryDecoder(nn.Module): t_emb = self.time_emb(t) c_emb = self.cond_enc(cond_cont, cond_cat, stage1_out) cond = torch.cat([t_emb, c_emb], dim=-1) - return _route_forward( - self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training - ) + return _route_forward(self.experts, self.router, x_t, cond, cond_cont, cond_cat, self.training) _STAGE1_MODEL_KEYS = { @@ -1006,9 +937,7 @@ def _parse_composed_axes(router_cfg: dict) -> list[dict]: _VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process") -def _check_router_conditioning_compat( - router_types: list[str], conditioning: str -) -> None: +def _check_router_conditioning_compat(router_types: list[str], conditioning: str) -> None: bad = sorted(set(router_types) & set(_VOCAB_SCOPED_ROUTER_TYPES)) if bad and conditioning == "physical": raise ValueError( @@ -1019,9 +948,7 @@ def _check_router_conditioning_compat( ) -def _build_router_from_cfg( - router_cfg: dict, pdg_vocab: int, mat_vocab: int, conditioning: str = "embedding" -) -> Router: +def _build_router_from_cfg(router_cfg: dict, pdg_vocab: int, mat_vocab: int, conditioning: str = "embedding") -> Router: shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab) if router_cfg["type"] == "composed": axes = _parse_composed_axes(router_cfg) @@ -1030,9 +957,7 @@ def _build_router_from_cfg( router.gumbel = bool(router_cfg.get("gumbel", False)) return router _check_router_conditioning_compat([router_cfg["type"]], conditioning) - router_kwargs = { - k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts") - } + router_kwargs = {k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts")} router_kwargs.setdefault("pdg_vocab", pdg_vocab) router_kwargs.setdefault("mat_vocab", mat_vocab) router = build_router(router_cfg["type"], router_cfg["n_experts"], **router_kwargs) @@ -1042,15 +967,9 @@ def _build_router_from_cfg( def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]: if model_config.get("mode") == "wgan": - stage1 = WGANGenerator( - **{k: v for k, v in model_config.items() if k in _WGAN_GENERATOR_MODEL_KEYS} - ) + stage1 = WGANGenerator(**{k: v for k, v in model_config.items() if k in _WGAN_GENERATOR_MODEL_KEYS}) sec_decoder = WGANSecondaryGenerator( - **{ - k: v - for k, v in model_config.items() - if k in _WGAN_SEC_GENERATOR_MODEL_KEYS - } + **{k: v for k, v in model_config.items() if k in _WGAN_SEC_GENERATOR_MODEL_KEYS} ) return stage1, sec_decoder @@ -1061,44 +980,30 @@ def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]: shared = dict( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, - expert_hidden_dim=model_config.get("expert_hidden_dim") - or model_config.get("hidden_dim", 128), - expert_n_blocks=model_config.get("expert_n_blocks") - or model_config.get("n_blocks", 3), + expert_hidden_dim=model_config.get("expert_hidden_dim") or model_config.get("hidden_dim", 128), + expert_n_blocks=model_config.get("expert_n_blocks") or model_config.get("n_blocks", 3), emb_dim=model_config.get("emb_dim", EMB_DIM), dropout=model_config.get("dropout", 0.1), conditioning=model_config.get("conditioning", "embedding"), ) conditioning = shared["conditioning"] stage1 = RoutedDenoisingMLP( - router=_build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, conditioning - ), + router=_build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, conditioning), k_max=model_config.get("k_max", K_MAX), **shared, ) sec_decoder = RoutedSecondaryDecoder( - router=_build_router_from_cfg( - router_cfg, pdg_vocab, mat_vocab, conditioning - ), + router=_build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, conditioning), **shared, ) return stage1, sec_decoder - stage1 = DenoisingMLP( - **{k: v for k, v in model_config.items() if k in _STAGE1_MODEL_KEYS} - ) - sec_decoder = SecondaryDecoder( - **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} - ) + stage1 = DenoisingMLP(**{k: v for k, v in model_config.items() if k in _STAGE1_MODEL_KEYS}) + sec_decoder = SecondaryDecoder(**{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS}) return stage1, sec_decoder def build_critics(model_config: dict) -> tuple[nn.Module, nn.Module]: - critic = Critic( - **{k: v for k, v in model_config.items() if k in _CRITIC_MODEL_KEYS} - ) - sec_critic = SecondaryCritic( - **{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS} - ) + critic = Critic(**{k: v for k, v in model_config.items() if k in _CRITIC_MODEL_KEYS}) + sec_critic = SecondaryCritic(**{k: v for k, v in model_config.items() if k in _SEC_DECODER_MODEL_KEYS}) return critic, sec_critic diff --git a/tests/test_bump_dataset_version.py b/tests/test_bump_dataset_version.py index 3f169a5..83f7dd9 100644 --- a/tests/test_bump_dataset_version.py +++ b/tests/test_bump_dataset_version.py @@ -22,9 +22,7 @@ def test_git_user_name_returns_none_on_timeout(monkeypatch): def test_bump_gen_starts_at_gen1_when_none_exist(tmp_path): - dirs, log_line = plan_bump_gen( - tmp_path, "steps", "first generation", None, "2026-01-01" - ) + dirs, log_line = plan_bump_gen(tmp_path, "steps", "first generation", None, "2026-01-01") assert dirs == [ tmp_path / "raw" / "steps" / "gen1", tmp_path / "processed" / "steps" / "gen1" / "schema1", @@ -56,9 +54,7 @@ def test_bump_gen_kinds_are_independent(tmp_path): def test_bump_schema_starts_at_schema1_for_a_fresh_gen(tmp_path): (tmp_path / "raw" / "steps" / "gen1").mkdir(parents=True) - dirs, log_line = plan_bump_schema( - tmp_path, "steps", "gen1", "added e_sec column", None, "2026-01-01" - ) + dirs, log_line = plan_bump_schema(tmp_path, "steps", "gen1", "added e_sec column", None, "2026-01-01") assert dirs == [tmp_path / "processed" / "steps" / "gen1" / "schema1"] assert "`gen1`/`schema1`" in log_line @@ -66,18 +62,14 @@ def test_bump_schema_starts_at_schema1_for_a_fresh_gen(tmp_path): def test_bump_schema_increments_within_its_gen(tmp_path): (tmp_path / "processed" / "steps" / "gen1" / "schema1").mkdir(parents=True) (tmp_path / "processed" / "steps" / "gen1" / "schema2").mkdir(parents=True) - dirs, _ = plan_bump_schema( - tmp_path, "steps", "gen1", "next schema", None, "2026-01-01" - ) + dirs, _ = plan_bump_schema(tmp_path, "steps", "gen1", "next schema", None, "2026-01-01") assert dirs == [tmp_path / "processed" / "steps" / "gen1" / "schema3"] def test_bump_schema_does_not_see_other_gens_schemas(tmp_path): (tmp_path / "processed" / "steps" / "gen1" / "schema5").mkdir(parents=True) (tmp_path / "raw" / "steps" / "gen2").mkdir(parents=True) - dirs, _ = plan_bump_schema( - tmp_path, "steps", "gen2", "fresh schema for gen2", None, "2026-01-01" - ) + dirs, _ = plan_bump_schema(tmp_path, "steps", "gen2", "fresh schema for gen2", None, "2026-01-01") assert dirs == [tmp_path / "processed" / "steps" / "gen2" / "schema1"] @@ -91,9 +83,7 @@ def test_bump_schema_rejects_nonexistent_gen(tmp_path): def test_bump_gen_to_specific_tag(tmp_path): (tmp_path / "raw" / "steps" / "gen1").mkdir(parents=True) - dirs, log_line = plan_bump_gen( - tmp_path, "steps", "jump to gen5", None, "2026-01-01", target="gen5" - ) + dirs, log_line = plan_bump_gen(tmp_path, "steps", "jump to gen5", None, "2026-01-01", target="gen5") assert dirs[0] == tmp_path / "raw" / "steps" / "gen5" assert "`gen5`" in log_line @@ -125,9 +115,7 @@ def test_bump_schema_to_specific_tag(tmp_path): def test_bump_schema_rejects_invalid_to_tag(tmp_path): (tmp_path / "raw" / "steps" / "gen1").mkdir(parents=True) try: - plan_bump_schema( - tmp_path, "steps", "gen1", "bad tag", None, "2026-01-01", target="v3" - ) + plan_bump_schema(tmp_path, "steps", "gen1", "bad tag", None, "2026-01-01", target="v3") assert False, "expected SystemExit" except SystemExit: pass @@ -164,15 +152,7 @@ def _make_parquet(path): def test_update_manifest_bumps_to_specified_schema(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -194,15 +174,7 @@ def test_update_manifest_auto_detects_highest_schema(tmp_path): for schema in ("schema1", "schema2", "schema3"): d = tmp_path / "processed" / "steps" / "gen1" / schema / "pbwo4" d.mkdir(parents=True) - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema3" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen1" / "schema3" / "pbwo4" / "shard-000.parquet" parquet.touch() manifest_dir = tmp_path / "pools" / "pbwo4" @@ -231,15 +203,7 @@ def test_update_manifest_reports_missing_targets(tmp_path): def test_update_manifest_skips_already_at_target(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -254,15 +218,7 @@ def test_update_manifest_skips_already_at_target(tmp_path): def test_update_manifest_preserves_comments_and_blanks(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -278,15 +234,7 @@ def test_update_manifest_preserves_comments_and_blanks(tmp_path): def test_update_manifest_bumps_gen(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen2" - / "schema1" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen2" / "schema1" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -303,15 +251,7 @@ def test_update_manifest_bumps_gen(tmp_path): def test_update_manifest_bumps_gen_and_schema(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen2" - / "schema3" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen2" / "schema3" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -328,15 +268,7 @@ def test_update_manifest_bumps_gen_and_schema(tmp_path): def test_apply_update_manifest_writes_file(tmp_path): - parquet = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-000.parquet" - ) + parquet = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-000.parquet" _make_parquet(parquet) manifest_dir = tmp_path / "pools" / "pbwo4" @@ -358,24 +290,8 @@ def test_apply_update_manifest_writes_file(tmp_path): def test_create_manifest_writes_relative_paths(tmp_path): - pq1 = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-000.parquet" - ) - pq2 = ( - tmp_path - / "processed" - / "steps" - / "gen1" - / "schema2" - / "pbwo4" - / "shard-001.parquet" - ) + pq1 = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-000.parquet" + pq2 = tmp_path / "processed" / "steps" / "gen1" / "schema2" / "pbwo4" / "shard-001.parquet" _make_parquet(pq1) _make_parquet(pq2) @@ -419,9 +335,7 @@ def test_run_create_manifest_refuses_to_overwrite_existing_output(tmp_path): output.write_text("original contents\n") try: - bump_dataset_version.run_create_manifest( - [str(pq)], execute=True, output=str(output) - ) + bump_dataset_version.run_create_manifest([str(pq)], execute=True, output=str(output)) assert False, "expected SystemExit" except SystemExit: pass @@ -435,9 +349,7 @@ def test_run_create_manifest_force_overwrites_existing_output(tmp_path): output.parent.mkdir(parents=True) output.write_text("original contents\n") - bump_dataset_version.run_create_manifest( - [str(pq)], execute=True, output=str(output), force=True - ) + bump_dataset_version.run_create_manifest([str(pq)], execute=True, output=str(output), force=True) assert output.read_text() != "original contents\n" diff --git a/tests/test_catalog.py b/tests/test_catalog.py index c8a47f0..92ddb24 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -13,9 +13,7 @@ from tests.test_analysis_reduce import _reference_frame, _rollout_frame def _build_ctx() -> Context: r, t = _rollout_frame(), _reference_frame() - return build_context( - r, t, n_energy_bins=2, n_marginal_bins=10, top_k_pdg=3, sample_rows=1000 - ) + return build_context(r, t, n_energy_bins=2, n_marginal_bins=10, top_k_pdg=3, sample_rows=1000) @pytest.fixture(scope="module") @@ -142,10 +140,7 @@ def test_chunked_matches_unchunked(ctx: Context, spec_id: str): # 4 chunks over only 2 distinct event_ids also exercises empty chunks. n_chunks = 4 if spec.chunkable else 1 - parts = [ - spec.compute_partial(Bundle.open(r, t, ctx, chunk=(k, n_chunks))) - for k in range(n_chunks) - ] + parts = [spec.compute_partial(Bundle.open(r, t, ctx, chunk=(k, n_chunks))) for k in range(n_chunks)] chunked = spec.finalize(parts, ctx) assert chunked.id == unchunked.id diff --git a/tests/test_cli_new_run.py b/tests/test_cli_new_run.py index c6ef14f..a4f9450 100644 --- a/tests/test_cli_new_run.py +++ b/tests/test_cli_new_run.py @@ -101,9 +101,7 @@ def test_force_guard_refuses_to_clobber_existing_checkpoints(tmp_path: Path): assert "already has last.pt" in result.output assert not (out_dir / "config.toml").exists() - result = runner.invoke( - app, ["new-run", "--out", str(out_dir), "--mode", "ddpm", "--force"] - ) + result = runner.invoke(app, ["new-run", "--out", str(out_dir), "--mode", "ddpm", "--force"]) assert result.exit_code == 0, result.output assert (out_dir / "config.toml").exists() diff --git a/tests/test_cli_predict.py b/tests/test_cli_predict.py index a6f74fb..1c18ae8 100644 --- a/tests/test_cli_predict.py +++ b/tests/test_cli_predict.py @@ -116,9 +116,7 @@ def test_ref_yaml_includes_comment_when_provided(tmp_path): dataset = tmp_path / "full.manifest" pred_uuid = str(uuid.uuid4()) - ref_path = _write_prediction_ref( - checkpoint, pred_uuid, out, dataset, comment="baseline sweep run 3" - ) + ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset, comment="baseline sweep run 3") data = yaml.safe_load(ref_path.read_text()) assert data["comment"] == "baseline sweep run 3" @@ -133,9 +131,7 @@ def test_ref_timestamp_is_iso_format(tmp_path): checkpoint.touch() pred_uuid = str(uuid.uuid4()) - ref_path = _write_prediction_ref( - checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d" - ) + ref_path = _write_prediction_ref(checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d") data = yaml.safe_load(ref_path.read_text()) # Must parse without error and be timezone-aware (UTC). @@ -150,9 +146,7 @@ def test_ref_checkpoint_path_is_absolute(tmp_path): checkpoint.touch() pred_uuid = str(uuid.uuid4()) - ref_path = _write_prediction_ref( - checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d" - ) + ref_path = _write_prediction_ref(checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d") data = yaml.safe_load(ref_path.read_text()) assert data["checkpoint"].startswith("/") diff --git a/tests/test_condor.py b/tests/test_condor.py index 4027a50..02912ed 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -62,9 +62,7 @@ def _fake_venv(repo_dir: Path) -> None: giant.chmod(0o755) -def _prep( - rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1 -) -> Path: +def _prep(rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1) -> Path: """``prep`` with small test-sized context bins/sampling.""" return prep( rollout_yaml, @@ -224,16 +222,12 @@ def test_write_submit_description(tmp_path: Path): assert "--chunk" in body and "--run-dir" in body -def test_write_submit_requires_synced_venv( - tmp_path: Path, monkeypatch: pytest.MonkeyPatch -): +def test_write_submit_requires_synced_venv(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): run_dir = _prep(_write_inputs(tmp_path)) cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path) # No `giant` next to the (fake) active interpreter, so this falls through # to repo_dir/.venv/bin/giant, which _write_inputs/_prep also didn't create. - monkeypatch.setattr( - sys, "executable", str(tmp_path / "not-a-venv" / "bin" / "python") - ) + monkeypatch.setattr(sys, "executable", str(tmp_path / "not-a-venv" / "bin" / "python")) with pytest.raises(FileNotFoundError, match="uv sync"): write_submit(cfg) @@ -241,9 +235,7 @@ def test_write_submit_requires_synced_venv( def test_write_submit_remote_flag(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path)) _fake_venv(tmp_path) - cfg = SubmitConfig( - run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True - ) + cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True) txt = write_submit(cfg).read_text() assert "+RemoteJob = True" in txt assert "ProvidesETPResources" not in txt @@ -253,9 +245,7 @@ def test_write_submit_chunks_respect_chunkable(tmp_path: Path): assert get_spec("router_gating").chunkable is False run_dir = _prep(_write_inputs(tmp_path), chunks=4) _fake_venv(tmp_path) - cfg = SubmitConfig( - run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4 - ) + cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4) write_submit(cfg) jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()] counts: dict[str, int] = {} @@ -272,9 +262,7 @@ def test_write_submit_rejects_n_chunks_mismatch_with_run_meta(tmp_path: Path): _job_walltimes instead of a clear error here.""" run_dir = _prep(_write_inputs(tmp_path), chunks=2) _fake_venv(tmp_path) - cfg = SubmitConfig( - run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4 - ) + cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4) with pytest.raises(ValueError, match="n_chunks"): write_submit(cfg) @@ -297,16 +285,9 @@ def test_write_submit_walltime_grows_with_chunk_rows(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path), chunks=2) meta = RunMeta.load(run_dir / "run_meta.json") _fake_venv(tmp_path) - cfg = SubmitConfig( - run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2 - ) + cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2) write_submit(cfg) - jobs = { - (i, int(k)): int(w) - for i, k, w in ( - line.split(",") for line in (run_dir / "jobs.txt").read_text().split() - ) - } + jobs = {(i, int(k)): int(w) for i, k, w in (line.split(",") for line in (run_dir / "jobs.txt").read_text().split())} for chunk in range(2): expected = estimate_runtime_s("marginal_edep", meta.rows_per_chunk[chunk]) assert jobs[("marginal_edep", chunk)] == expected diff --git a/tests/test_config.py b/tests/test_config.py index fb7b5c2..6aea480 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -112,9 +112,7 @@ def test_migrate_config_lambda_nsec_and_lambda_s2(): def test_migrate_config_wgan_knobs_map_to_both_stages(): - new = gconfig.migrate_config( - {"train": {"n_critic": 3, "gp_weight": 5.0, "critic_lr": 1e-4}} - ) + new = gconfig.migrate_config({"train": {"n_critic": 3, "gp_weight": 5.0, "critic_lr": 1e-4}}) for stage in ("stage1_model", "stage2_model"): assert new[stage]["wgan"]["n_critic"] == 3 assert new[stage]["wgan"]["gp_weight"] == 5.0 @@ -122,9 +120,7 @@ def test_migrate_config_wgan_knobs_map_to_both_stages(): def test_migrate_config_model_hidden_dim_n_blocks_dropout_map_to_both_stages(): - new = gconfig.migrate_config( - {"model": {"hidden_dim": 128, "n_blocks": 4, "dropout": 0.2}} - ) + new = gconfig.migrate_config({"model": {"hidden_dim": 128, "n_blocks": 4, "dropout": 0.2}}) for stage in ("stage1_model", "stage2_model"): assert new[stage]["hidden_dim"] == 128 assert new[stage]["n_res_blocks"] == 4 @@ -132,9 +128,7 @@ def test_migrate_config_model_hidden_dim_n_blocks_dropout_map_to_both_stages(): def test_migrate_config_emb_dim_and_conditioning_map_to_both_axes(): - new = gconfig.migrate_config( - {"model": {"emb_dim": 32, "conditioning": "embedding"}} - ) + new = gconfig.migrate_config({"model": {"emb_dim": 32, "conditioning": "embedding"}}) for axis in ("particle", "material"): assert new["conditioning"][axis]["emb_dim"] == 32 assert new["conditioning"][axis]["type"] == "embedding" @@ -181,11 +175,7 @@ def test_migrate_config_router_copied_to_both_stages_with_tie_to_stage1_false(): def test_migrate_config_router_nonzero_expert_dims_raises(): - cfg = { - "model": { - "router": {"enabled": True, "expert_hidden_dim": 128, "expert_n_blocks": 0} - } - } + cfg = {"model": {"router": {"enabled": True, "expert_hidden_dim": 128, "expert_n_blocks": 0}}} try: gconfig.migrate_config(cfg) assert False, "expected ValueError" @@ -267,9 +257,7 @@ def test_merge_cli_overrides_nested_override_keeps_siblings(): assert cfg["stage1_model"]["hidden_dim"] == 256 # untouched sibling section -def test_merge_cli_overrides_file_then_explicit_override_precedence( - tmp_path, monkeypatch -): +def test_merge_cli_overrides_file_then_explicit_override_precedence(tmp_path, monkeypatch): monkeypatch.setattr(gconfig, "git_hash", lambda: "abc123") path = tmp_path / "config.toml" _write_toml( @@ -317,9 +305,7 @@ def test_merge_cli_overrides_warns_on_git_hash_mismatch(tmp_path, monkeypatch, c assert "current999" in captured.err -def test_merge_cli_overrides_no_warning_on_matching_git_hash( - tmp_path, monkeypatch, capsys -): +def test_merge_cli_overrides_no_warning_on_matching_git_hash(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "same123") path = tmp_path / "config.toml" _write_toml(path, git_hash="same123", extra="[train]\nepochs = 5\n") @@ -328,9 +314,7 @@ def test_merge_cli_overrides_no_warning_on_matching_git_hash( assert capsys.readouterr().err == "" -def test_merge_cli_overrides_no_warning_when_git_hash_unknown( - tmp_path, monkeypatch, capsys -): +def test_merge_cli_overrides_no_warning_when_git_hash_unknown(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "unknown") path = tmp_path / "config.toml" _write_toml(path, git_hash="abc123", extra="[train]\nepochs = 5\n") @@ -339,9 +323,7 @@ def test_merge_cli_overrides_no_warning_when_git_hash_unknown( assert capsys.readouterr().err == "" -def test_merge_cli_overrides_no_warning_when_meta_section_absent( - tmp_path, monkeypatch, capsys -): +def test_merge_cli_overrides_no_warning_when_meta_section_absent(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "current999") path = tmp_path / "config.toml" path.write_text("[train]\nepochs = 5\n") @@ -351,12 +333,8 @@ def test_merge_cli_overrides_no_warning_when_meta_section_absent( def test_merge_cli_overrides_real_default_toml_fixture(monkeypatch): - monkeypatch.setattr( - gconfig, "git_hash", lambda: "c3bf3abebfe29a10fe42b9cbafbb3460ab78d243" - ) - cfg = gconfig.merge_cli_overrides( - gconfig.DEFAULT_CONFIG, _CONFIGS_DIR / "default.toml", {} - ) + monkeypatch.setattr(gconfig, "git_hash", lambda: "c3bf3abebfe29a10fe42b9cbafbb3460ab78d243") + cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, _CONFIGS_DIR / "default.toml", {}) assert cfg["stage1_model"]["generator"] == "flow" assert cfg["stage1_model"]["hidden_dim"] == 256 assert cfg["stage2_model"]["hidden_dim"] == 256 @@ -431,10 +409,7 @@ def _cfg_with(**dotted_overrides): def test_default_out_dir_name_all_defaults_is_just_the_timestamp(): - assert ( - gconfig.default_out_dir_name(gconfig.DEFAULT_CONFIG, now=_NOW) - == "20260729_1430" - ) + assert gconfig.default_out_dir_name(gconfig.DEFAULT_CONFIG, now=_NOW) == "20260729_1430" def test_default_out_dir_name_stage1_generator_shown_bare_no_prefix(): @@ -499,9 +474,7 @@ def test_default_out_dir_name_router_gumbel_shown_when_enabled(): "stage1_model.router.gumbel": True, } ) - assert ( - gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_s1r-energy8_s1gum" - ) + assert gconfig.default_out_dir_name(cfg, now=_NOW) == "20260729_1430_s1r-energy8_s1gum" def test_default_out_dir_name_overflow_caps_and_hashes_remainder(): @@ -524,9 +497,7 @@ def test_default_out_dir_name_overflow_caps_and_hashes_remainder(): # First 6 by priority: stage1_generator, stage2_generator, stage2_decoder, # stage2_history, particle_type_target, stage1_router — stage2_router # overflows into the hash suffix. - assert name.startswith( - "20260729_1430_wgan_s2-flow_dec-one_shot_hist-attention_pt-physical_s1r-energy8_+" - ) + assert name.startswith("20260729_1430_wgan_s2-flow_dec-one_shot_hist-attention_pt-physical_s1r-energy8_+") def test_default_out_dir_name_overflow_hash_is_deterministic_and_value_sensitive(): @@ -758,15 +729,11 @@ def test_validate_config_ar_checks_skipped_under_one_shot(): # --------------------------------------------------------------------------- -def test_warn_if_checkpoint_config_mismatch_finds_sibling_toml( - tmp_path, monkeypatch, capsys -): +def test_warn_if_checkpoint_config_mismatch_finds_sibling_toml(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "current999") ckpt_path = tmp_path / "best.pt" ckpt_path.write_bytes(b"") - _write_toml( - tmp_path / "config.toml", git_hash="old111", extra="[train]\nepochs = 5\n" - ) + _write_toml(tmp_path / "config.toml", git_hash="old111", extra="[train]\nepochs = 5\n") gconfig.warn_if_checkpoint_config_mismatch(ckpt_path) @@ -776,9 +743,7 @@ def test_warn_if_checkpoint_config_mismatch_finds_sibling_toml( assert "current999" in captured.err -def test_warn_if_checkpoint_config_mismatch_no_warning_when_toml_absent( - tmp_path, monkeypatch, capsys -): +def test_warn_if_checkpoint_config_mismatch_no_warning_when_toml_absent(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "current999") ckpt_path = tmp_path / "best.pt" ckpt_path.write_bytes(b"") @@ -787,15 +752,11 @@ def test_warn_if_checkpoint_config_mismatch_no_warning_when_toml_absent( assert capsys.readouterr().err == "" -def test_warn_if_checkpoint_config_mismatch_no_warning_when_hashes_match( - tmp_path, monkeypatch, capsys -): +def test_warn_if_checkpoint_config_mismatch_no_warning_when_hashes_match(tmp_path, monkeypatch, capsys): monkeypatch.setattr(gconfig, "git_hash", lambda: "same123") ckpt_path = tmp_path / "best.pt" ckpt_path.write_bytes(b"") - _write_toml( - tmp_path / "config.toml", git_hash="same123", extra="[train]\nepochs = 5\n" - ) + _write_toml(tmp_path / "config.toml", git_hash="same123", extra="[train]\nepochs = 5\n") gconfig.warn_if_checkpoint_config_mismatch(ckpt_path) assert capsys.readouterr().err == "" diff --git a/tests/test_create_root_files.py b/tests/test_create_root_files.py index f07453c..686e198 100644 --- a/tests/test_create_root_files.py +++ b/tests/test_create_root_files.py @@ -16,9 +16,7 @@ SimJob = create_root_files.SimJob PlanError = create_root_files.PlanError -def _write_fake_executable( - path: Path, *, output_count: int = 1, exit_code: int = 0, sleep: float = 0.0 -) -> Path: +def _write_fake_executable(path: Path, *, output_count: int = 1, exit_code: int = 0, sleep: float = 0.0) -> Path: """Stand-in for run_pbwo4/run_sampling: writes *output_count* .root files into its own cwd (so callers can verify each job gets an isolated workdir and that the workdir ends up holding *only* the .root output, matching @@ -89,17 +87,13 @@ def test_next_shard_index_continues_past_existing(tmp_path): def test_plan_jobs_rejects_missing_gen(tmp_path): with pytest.raises(PlanError): - plan_jobs( - ["pbwo4"], num_files=2, dataset_root=tmp_path, kind="steps", gen="gen1" - ) + plan_jobs(["pbwo4"], num_files=2, dataset_root=tmp_path, kind="steps", gen="gen1") def test_plan_jobs_rejects_malformed_gen(tmp_path): (tmp_path / "raw" / "steps" / "gen1").mkdir(parents=True) with pytest.raises(PlanError): - plan_jobs( - ["pbwo4"], num_files=2, dataset_root=tmp_path, kind="steps", gen="notagen" - ) + plan_jobs(["pbwo4"], num_files=2, dataset_root=tmp_path, kind="steps", gen="notagen") def test_plan_jobs_continues_from_existing_shards(tmp_path): @@ -108,9 +102,7 @@ def test_plan_jobs_continues_from_existing_shards(tmp_path): (gen_dir / "pbwo4" / "shard-000.root").touch() (gen_dir / "pbwo4" / "shard-001.root").touch() - jobs = plan_jobs( - ["pbwo4"], num_files=3, dataset_root=tmp_path, kind="steps", gen="gen1" - ) + jobs = plan_jobs(["pbwo4"], num_files=3, dataset_root=tmp_path, kind="steps", gen="gen1") assert [j.shard_index for j in jobs] == [2, 3, 4] assert all(j.detector == "pbwo4" and j.config is None for j in jobs) @@ -320,9 +312,7 @@ def test_run_all_caps_concurrency(tmp_path): assert {d.name for d in dests} == {f"shard-{i:03d}.root" for i in range(6)} intervals = [json.loads(d.read_text()) for d in dests] - events = sorted( - [(p["start"], 1) for p in intervals] + [(p["end"], -1) for p in intervals] - ) + events = sorted([(p["start"], 1) for p in intervals] + [(p["end"], -1) for p in intervals]) concurrent = 0 peak = 0 for _, delta in events: diff --git a/tests/test_dwarf.py b/tests/test_dwarf.py index 767828e..2d9aeae 100644 --- a/tests/test_dwarf.py +++ b/tests/test_dwarf.py @@ -51,9 +51,7 @@ def test_convert_rejects_output_with_multiple_files(tmp_path): def test_convert_rejects_output_with_parallel_jobs(tmp_path): root_file = tmp_path / "shard.root" root_file.touch() - result = runner.invoke( - app, ["convert", str(root_file), "--output", "out.parquet", "--jobs", "2"] - ) + result = runner.invoke(app, ["convert", str(root_file), "--output", "out.parquet", "--jobs", "2"]) assert result.exit_code != 0 assert "--output cannot be combined with --jobs > 1" in result.output diff --git a/tests/test_geometry.py b/tests/test_geometry.py index 9e9aace..aa67110 100644 --- a/tests/test_geometry.py +++ b/tests/test_geometry.py @@ -44,9 +44,7 @@ def test_iter_point_batches_without_post_columns_yields_pre_only(tmp_path): (pos, mat, lay) = next(g._iter_point_batches(path)) assert pos.shape == (5, 3) - np.testing.assert_allclose( - pos, df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32) - ) + np.testing.assert_allclose(pos, df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32)) assert list(mat) == ["G4_AIR"] * 5 np.testing.assert_array_equal(lay, np.arange(5)) @@ -63,12 +61,8 @@ def test_iter_point_batches_with_post_columns_doubles_and_concatenates_points( # Every step contributes both its pre_pos and post_pos, sharing the # step's material/layer_id label — so batches double in length. assert pos.shape == (10, 3) - np.testing.assert_allclose( - pos[:5], df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32) - ) - np.testing.assert_allclose( - pos[5:], df[["post_x", "post_y", "post_z"]].to_numpy(dtype=np.float32) - ) + np.testing.assert_allclose(pos[:5], df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32)) + np.testing.assert_allclose(pos[5:], df[["post_x", "post_y", "post_z"]].to_numpy(dtype=np.float32)) assert list(mat) == ["G4_AIR"] * 10 np.testing.assert_array_equal(lay, np.concatenate([np.arange(5), np.arange(5)])) @@ -208,9 +202,7 @@ def test_slab_classes_discovered(): def test_slab_query_labels_by_depth(): orc = _build_slab() - pos = np.array( - [[0.0, 0.0, 50.0], [0.0, 0.0, 105.0], [0.0, 0.0, 150.0]] - ) # layer 0, gap, layer 1 + pos = np.array([[0.0, 0.0, 50.0], [0.0, 0.0, 105.0], [0.0, 0.0, 150.0]]) # layer 0, gap, layer 1 material, layer_id, escaped = orc.query(pos) assert list(material) == ["G4_PbWO4", "G4_AIR", "G4_W"] assert list(layer_id) == [0, -1, 1] @@ -239,9 +231,7 @@ def test_slab_save_load_roundtrip(tmp_path): orc.save(p) loaded = g.GeometryOracle.load(p) - pos = np.array( - [[0.0, 0.0, 50.0], [0.0, 0.0, 105.0], [0.0, 0.0, 150.0], [0.0, 0.0, 1e5]] - ) + pos = np.array([[0.0, 0.0, 50.0], [0.0, 0.0, 105.0], [0.0, 0.0, 150.0], [0.0, 0.0, 1e5]]) m0, l0, e0 = orc.query(pos) m1, l1, e1 = loaded.query(pos) assert (m0 == m1).all() and (l0 == l1).all() and (e0 == e1).all() diff --git a/tests/test_loader.py b/tests/test_loader.py index ad31f7a..5d57656 100644 --- a/tests/test_loader.py +++ b/tests/test_loader.py @@ -315,9 +315,7 @@ def test_build_index_maps_from_files_ordering_independent_of_file_order(tmp_path def test_build_index_maps_from_files_numeric_sort_for_nuclear_codes(tmp_path): path = tmp_path / "a.parquet" - pd.DataFrame({"pdg": [22, 1000060120, 11], "material": ["X", "X", "X"]}).to_parquet( - path - ) + pd.DataFrame({"pdg": [22, 1000060120, 11], "material": ["X", "X", "X"]}).to_parquet(path) pdg_map, _ = build_index_maps_from_files([path]) assert list(pdg_map.keys()) == [11, 22, 1000060120] @@ -398,9 +396,7 @@ def test_load_event_ids_applies_offset(tmp_path): path = tmp_path / "a.parquet" pd.DataFrame({"event_id": [0, 1, 2]}).to_parquet(path) offset = event_id_offset(1) - np.testing.assert_array_equal( - load_event_ids(path, offset=offset), [offset, offset + 1, offset + 2] - ) + np.testing.assert_array_equal(load_event_ids(path, offset=offset), [offset, offset + 1, offset + 2]) def test_load_event_ids_raises_when_event_id_reaches_stride(tmp_path): diff --git a/tests/test_materials.py b/tests/test_materials.py index c0d0c8e..233bf93 100644 --- a/tests/test_materials.py +++ b/tests/test_materials.py @@ -23,11 +23,7 @@ def test_get_material_properties_unfilled_entry_raises(): def test_get_material_properties_returns_filled_entry_from_injected_table(): - table = { - "G4_Pb": MaterialProperties( - z_eff=82.0, a_eff=207.2, density=11.35, x0=0.5612, lambda_int=17.59 - ) - } + table = {"G4_Pb": MaterialProperties(z_eff=82.0, a_eff=207.2, density=11.35, x0=0.5612, lambda_int=17.59)} props = get_material_properties("G4_Pb", table) assert props.z_eff == 82.0 assert props.a_eff == 207.2 diff --git a/tests/test_migration_v02_v03.py b/tests/test_migration_v02_v03.py index 05829a3..35f4db5 100644 --- a/tests/test_migration_v02_v03.py +++ b/tests/test_migration_v02_v03.py @@ -56,9 +56,7 @@ def _random_batch(seed: int): def _assert_bit_identical(a: torch.Tensor, b: torch.Tensor, label: str) -> None: assert a.shape == b.shape, f"{label}: shape mismatch {a.shape} vs {b.shape}" - assert torch.equal(a, b), ( - f"{label}: outputs diverged, max abs diff = {(a - b).abs().max().item()}" - ) + assert torch.equal(a, b), f"{label}: outputs diverged, max abs diff = {(a - b).abs().max().item()}" def _run_migration_check(mode: str, conditioning: str) -> None: @@ -137,9 +135,7 @@ def _run_migration_check(mode: str, conditioning: str) -> None: assert new_stage1.n_sec_head is not None assert new_stage2.n_sec_head is None - remapped1, remapped2 = net.migrate_legacy_state_dict( - old_stage1.state_dict(), old_stage2.state_dict() - ) + remapped1, remapped2 = net.migrate_legacy_state_dict(old_stage1.state_dict(), old_stage2.state_dict()) missing1, unexpected1 = new_stage1.load_state_dict(remapped1, strict=True) missing2, unexpected2 = new_stage2.load_state_dict(remapped2, strict=True) assert not missing1 and not unexpected1 @@ -159,9 +155,7 @@ def _run_migration_check(mode: str, conditioning: str) -> None: new_out2 = new_stage2(x2, cond_cont, cond_cat, new_out1, t=t) _assert_bit_identical(old_out1, new_out1, f"stage1 output ({mode}, {conditioning})") - _assert_bit_identical( - old_n_sec, new_n_sec, f"n_sec logits ({mode}, {conditioning})" - ) + _assert_bit_identical(old_n_sec, new_n_sec, f"n_sec logits ({mode}, {conditioning})") _assert_bit_identical(old_out2, new_out2, f"stage2 output ({mode}, {conditioning})") diff --git a/tests/test_network.py b/tests/test_network.py index bbf0552..5deab01 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -85,9 +85,7 @@ def test_stage1_model_gradients_flow(): def test_stage1_model_no_n_sec_head_by_default(): """Fresh v0.3.0 construction (no n_sec_head_k_max) has no n_sec head — it moves to stage 2.""" - model = Stage1Model( - pdg_vocab=3, mat_vocab=2, particle_cfg=PARTICLE_CFG, material_cfg=MATERIAL_CFG - ) + model = Stage1Model(pdg_vocab=3, mat_vocab=2, particle_cfg=PARTICLE_CFG, material_cfg=MATERIAL_CFG) assert model.n_sec_head is None @@ -121,29 +119,18 @@ def test_stage2_type_dim_onehot_and_embedding_are_emb_dim(): def test_stage2_trunk_sec_dim_physical_matches_v02_sec_dim(): k_max = 15 - assert ( - stage2_trunk_sec_dim({"target": "physical"}, "flow", k_max, emb_dim=16) - == k_max * SEC_SLOT_DIM - ) - assert ( - stage2_trunk_sec_dim({"target": "physical"}, "wgan", k_max, emb_dim=16) - == k_max * SEC_SLOT_DIM - ) + assert stage2_trunk_sec_dim({"target": "physical"}, "flow", k_max, emb_dim=16) == k_max * SEC_SLOT_DIM + assert stage2_trunk_sec_dim({"target": "physical"}, "wgan", k_max, emb_dim=16) == k_max * SEC_SLOT_DIM def test_stage2_trunk_sec_dim_onehot_wgan_folds_type_in(): k_max = 15 - assert stage2_trunk_sec_dim( - {"target": "onehot"}, "wgan", k_max, emb_dim=16 - ) == k_max * (CONT_SLOT_DIM + 16) + assert stage2_trunk_sec_dim({"target": "onehot"}, "wgan", k_max, emb_dim=16) == k_max * (CONT_SLOT_DIM + 16) def test_stage2_trunk_sec_dim_onehot_flow_excludes_type(): k_max = 15 - assert ( - stage2_trunk_sec_dim({"target": "onehot"}, "flow", k_max, emb_dim=16) - == k_max * CONT_SLOT_DIM - ) + assert stage2_trunk_sec_dim({"target": "onehot"}, "flow", k_max, emb_dim=16) == k_max * CONT_SLOT_DIM # --- ConditionEncoder onehot mode ------------------------------------------- @@ -440,16 +427,12 @@ def test_stage2_autoregressive_history_invalid_raises(): @pytest.mark.parametrize("history", ["markov", "attention"]) def test_stage2_autoregressive_forward_shape(target, generator, history): B, K, emb_dim = 4, 5, 6 - model = _build_stage2_ar( - target, generator, emb_dim=emb_dim, k_max=K, history=history - ) + model = _build_stage2_ar(target, generator, emb_dim=emb_dim, k_max=K, history=history) cond_cont = torch.randn(B, COND_DIM) cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) type_dim = stage2_type_dim({"target": target}, emb_dim) - history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( - B, K, CONT_SLOT_DIM + type_dim - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs(B, K, CONT_SLOT_DIM + type_dim) token_dim = stage2_trunk_sec_dim({"target": target}, generator, 1, emb_dim) if generator == "wgan": x_t = torch.randn(B, K, model.noise_dim) @@ -488,9 +471,7 @@ def test_stage2_autoregressive_predict_type_shape(): cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) - history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( - B, K, CONT_SLOT_DIM + type_dim - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs(B, K, CONT_SLOT_DIM + type_dim) out = model.predict_type( cond_cont, cond_cat, @@ -511,9 +492,7 @@ def test_stage2_autoregressive_predict_type_raises_when_no_type_head(target, gen cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) type_dim = stage2_type_dim({"target": target}, emb_dim) - history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( - B, K, CONT_SLOT_DIM + type_dim - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs(B, K, CONT_SLOT_DIM + type_dim) with pytest.raises(RuntimeError): model.predict_type( cond_cont, @@ -533,9 +512,7 @@ def test_stage2_autoregressive_gradients_flow_wgan_onehot(): cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) - history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( - B, K, CONT_SLOT_DIM + type_dim - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs(B, K, CONT_SLOT_DIM + type_dim) z = torch.randn(B, K, model.noise_dim) gen_out = model( z, @@ -560,9 +537,7 @@ def test_stage2_autoregressive_gradients_flow_onehot(): cond_cat = torch.zeros(B, 2, dtype=torch.long) stage1_out = torch.randn(B, 9) type_dim = stage2_type_dim({"target": "onehot"}, emb_dim) - history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs( - B, K, CONT_SLOT_DIM + type_dim - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_inputs(B, K, CONT_SLOT_DIM + type_dim) token_dim = stage2_trunk_sec_dim({"target": "onehot"}, "flow", 1, emb_dim) x_t = torch.randn(B, K, token_dim) t = torch.rand(B, K) @@ -601,17 +576,13 @@ def test_stage2_autoregressive_history_step_matches_parallel_history_encoder(): itself rather than `AttentionHistory` in isolation (`test_attention_history_step_matches_forward` covers that lower layer).""" B, K, emb_dim = 3, 6, 6 - model = _build_stage2_ar( - "physical", "wgan", emb_dim=emb_dim, k_max=K, history="attention" - ) + model = _build_stage2_ar("physical", "wgan", emb_dim=emb_dim, k_max=K, history="attention") model.eval() type_dim = stage2_type_dim({"target": "physical"}, emb_dim) hist_in_dim = CONT_SLOT_DIM + type_dim own_feat = torch.randn(B, K, hist_in_dim) # token i's own raw feature has_prev_full = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1) - history_feat = torch.cat( - [torch.zeros_like(own_feat[:, :1]), own_feat[:, :-1]], dim=1 - ) + history_feat = torch.cat([torch.zeros_like(own_feat[:, :1]), own_feat[:, :-1]], dim=1) with torch.no_grad(): expected = model.history_encoder(history_feat, has_prev_full) diff --git a/tests/test_particles.py b/tests/test_particles.py index bcfbfdf..c80e724 100644 --- a/tests/test_particles.py +++ b/tests/test_particles.py @@ -49,9 +49,7 @@ def test_ground_state_nucleus_resolved_via_particle_package(): """He-4 (Z=2, A=4) is a common nuclide in `particle`'s ground-state table.""" mass, charge = particle_mass_charge(1000020040) assert charge == pytest.approx(2.0) - assert mass == pytest.approx( - 4 * 931.494, rel=0.05 - ) # near A*amu, binding-energy-corrected + assert mass == pytest.approx(4 * 931.494, rel=0.05) # near A*amu, binding-energy-corrected def test_nuclear_isomer_falls_back_to_z_a_decode(): @@ -174,9 +172,7 @@ def test_decode_topn_class_other_drop_returns_zero_sentinel(): def test_decode_topn_class_other_sample_stays_within_members(): topn_map, n_classes = _topn_fixture() rng = np.random.default_rng(0) - out = decode_topn_class( - np.full(50, 3), topn_map, n_classes, other_policy="sample", rng=rng - ) + out = decode_topn_class(np.full(50, 3), topn_map, n_classes, other_policy="sample", rng=rng) assert set(out.tolist()) <= {2212, 2112} diff --git a/tests/test_phase2.py b/tests/test_phase2.py index afb12bd..7a8efec 100644 --- a/tests/test_phase2.py +++ b/tests/test_phase2.py @@ -57,9 +57,7 @@ def _sec_decoder(pdg=3, mat=2, conditioning="embedding"): def _cond(B=8, pdg=3, mat=2): cond_cont = torch.randn(B, COND_DIM) - cond_cat = torch.stack( - [torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1 - ) + cond_cat = torch.stack([torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1) return cond_cont, cond_cat @@ -154,9 +152,7 @@ def test_flow_matching_loss_secondary_scalar(): cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) sec_mask = torch.ones(B, K_MAX, dtype=torch.bool) - loss = flow_matching_loss_secondary( - decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask - ) + loss = flow_matching_loss_secondary(decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask) assert loss.shape == () assert loss.item() >= 0.0 @@ -169,9 +165,7 @@ def test_flow_matching_loss_secondary_mask_zeros_padding(): cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) sec_mask = torch.zeros(B, K_MAX, dtype=torch.bool) - loss = flow_matching_loss_secondary( - decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask - ) + loss = flow_matching_loss_secondary(decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask) assert loss.item() == pytest.approx(0.0, abs=1e-6) @@ -182,9 +176,7 @@ def test_flow_matching_loss_secondary_has_grad(): cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) sec_mask = torch.ones(B, K_MAX, dtype=torch.bool) - flow_matching_loss_secondary( - decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask - ).backward() + flow_matching_loss_secondary(decoder, x1, cond_cont, cond_cat, stage1_out, sec_mask).backward() assert any(p.grad is not None for p in decoder.parameters()) @@ -220,9 +212,7 @@ def test_flow_matching_loss_secondary_ar_scalar(): x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) - history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( - B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) sec_mask = torch.ones(B, K, dtype=torch.bool) loss = flow_matching_loss_secondary_ar( decoder, @@ -246,9 +236,7 @@ def test_flow_matching_loss_secondary_ar_mask_zeros_padding(): x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) - history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( - B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) sec_mask = torch.zeros(B, K, dtype=torch.bool) loss = flow_matching_loss_secondary_ar( decoder, @@ -271,9 +259,7 @@ def test_flow_matching_loss_secondary_ar_has_grad(): x1 = torch.randn(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) - history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs( - B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM - ) + history_feat, has_prev, remaining_frac, slot_idx = _ar_history_inputs(B, K, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) sec_mask = torch.ones(B, K, dtype=torch.bool) flow_matching_loss_secondary_ar( decoder, @@ -299,9 +285,7 @@ def test_sample_secondaries_shapes(): cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, K_MAX + 1, (B,)) - sec_cont, sec_phys, sec_valid = sample_secondaries( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=3 - ) + sec_cont, sec_phys, sec_valid = sample_secondaries(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=3) assert sec_cont.shape == (B, K_MAX, 4) assert sec_phys.shape == (B, K_MAX, PARTICLE_PHYS_DIM) assert sec_valid.shape == (B, K_MAX) @@ -314,9 +298,7 @@ def test_sample_secondaries_valid_mask_matches_n_sec(): cond_cont, cond_cat = _cond(B, pdg, mat) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.tensor([0, 1, 3, K_MAX]) - _, _, sec_valid = sample_secondaries( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 - ) + _, _, sec_valid = sample_secondaries(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2) for i, n in enumerate(n_sec_pred.tolist()): assert sec_valid[i, :n].all() assert not sec_valid[i, n:].any() @@ -350,9 +332,7 @@ def test_encode_secondaries_energy_conservation(): pre_dir = rng.standard_normal((N, 3)).astype(np.float32) pre_dir /= np.linalg.norm(pre_dir, axis=1, keepdims=True) - sec_cont = encode_secondaries( - sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list - ) + sec_cont = encode_secondaries(sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list) assert sec_cont.shape == (N, K_MAX, 6) assert np.isfinite(sec_cont).all() @@ -399,9 +379,7 @@ def test_encode_secondaries_stick_logits_match_naive_reference(): logit = np.clip(logit, -_STICK_LOGIT_CLIP, _STICK_LOGIT_CLIP) expected[row, i] = logit - np.testing.assert_allclose( - stick_logits[sec_valid], expected[sec_valid], rtol=1e-4, atol=1e-4 - ) + np.testing.assert_allclose(stick_logits[sec_valid], expected[sec_valid], rtol=1e-4, atol=1e-4) def test_encode_secondaries_direction_encoding(): @@ -513,9 +491,7 @@ def test_encode_secondaries_physical_columns_match_ground_truth_pdg(): sec_valid[0, 0] = True pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32) - sec_cont = encode_secondaries( - sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list - ) + sec_cont = encode_secondaries(sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list) mass, charge = particle_mass_charge(11) assert sec_cont[0, 0, 4] == pytest.approx(log_transform(np.array([mass]))[0]) assert sec_cont[0, 0, 5] == pytest.approx(charge) @@ -549,9 +525,7 @@ def test_decode_secondaries_valid_slots_sum_to_e_sec(): e_sec = rng.uniform(0.0, 50.0, size=N).astype(np.float32) pre_dir = np.tile([0.0, 0.0, 1.0], (N, 1)).astype(np.float32) - sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries( - sec_cont, n_sec, e_sec, pre_dir - ) + sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir) valid_sum = (sec_E * sec_valid).sum(axis=1) has_secondaries = n_sec > 0 @@ -574,9 +548,7 @@ def test_decode_secondaries_zero_n_sec_has_zero_energy(): e_sec = rng.uniform(1.0, 10.0, size=N).astype(np.float32) pre_dir = np.tile([0.0, 0.0, 1.0], (N, 1)).astype(np.float32) - sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries( - sec_cont, n_sec, e_sec, pre_dir - ) + sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir) assert not sec_valid.any() np.testing.assert_allclose(sec_E, 0.0) @@ -596,9 +568,7 @@ def test_decode_secondaries_degenerate_row_falls_back_to_even_split(): e_sec = np.array([0.0, 4.0, 9.0, 30.0], dtype=np.float32) pre_dir = np.tile([0.0, 0.0, 1.0], (N, 1)).astype(np.float32) - sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries( - sec_cont, n_sec, e_sec, pre_dir - ) + sec_E, _sec_dir, _mass, _charge, sec_valid = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir) for i, k in enumerate(n_sec): if k == 0: @@ -622,12 +592,8 @@ def test_decode_secondaries_rescale_preserves_relative_shares(): n_sec = np.array([4]) pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32) - sec_E_small, _, _, _, sec_valid = decode_secondaries( - sec_cont, n_sec, np.array([5.0], dtype=np.float32), pre_dir - ) - sec_E_large, _, _, _, _ = decode_secondaries( - sec_cont, n_sec, np.array([50.0], dtype=np.float32), pre_dir - ) + sec_E_small, _, _, _, sec_valid = decode_secondaries(sec_cont, n_sec, np.array([5.0], dtype=np.float32), pre_dir) + sec_E_large, _, _, _, _ = decode_secondaries(sec_cont, n_sec, np.array([50.0], dtype=np.float32), pre_dir) ratio_small = sec_E_small[0, :4] / sec_E_small[0, 0] ratio_large = sec_E_large[0, :4] / sec_E_large[0, 0] @@ -649,20 +615,14 @@ def test_decode_secondaries_mass_charge_round_trip_with_normalizer(): sec_valid[0, 0] = True pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32) - sec_cont = encode_secondaries( - sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list - ) + sec_cont = encode_secondaries(sec_E_list, sec_dir_list, sec_valid, e_sec, pre_dir, sec_pdg_list=sec_pdg_list) norm = Normalizer() norm.mean = np.array([-2.0, 0.5], dtype=np.float32) norm.std = np.array([3.0, 1.5], dtype=np.float32) sec_cont_normed = sec_cont.copy() - sec_cont_normed[:, :, 4:6] = norm.transform( - sec_cont[:, :, 4:6].reshape(-1, 2) - ).reshape(N, K_MAX, 2) + sec_cont_normed[:, :, 4:6] = norm.transform(sec_cont[:, :, 4:6].reshape(-1, 2)).reshape(N, K_MAX, 2) n_sec = np.array([1]) - _, _, sec_mass, sec_charge, _ = decode_secondaries( - sec_cont_normed, n_sec, e_sec, pre_dir, sec_phys_normalizer=norm - ) + _, _, sec_mass, sec_charge, _ = decode_secondaries(sec_cont_normed, n_sec, e_sec, pre_dir, sec_phys_normalizer=norm) assert sec_mass[0, 0] == pytest.approx(938.27208943, abs=1e-2) assert sec_charge[0, 0] == pytest.approx(1.0, abs=1e-4) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index c0848c9..9c25e67 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -48,9 +48,7 @@ def _make_synthetic_steps(path, n_events=20, seed=0): pre_dir = np.array([0.0, 0.0, 1.0]) post_dir = _unit(rng.normal(size=3)) post_pos = pre_pos + step_length * pre_dir - sec_energies = ( - list(rng.dirichlet(np.ones(n_sec)) * e_sec) if n_sec > 0 else [] - ) + sec_energies = list(rng.dirichlet(np.ones(n_sec)) * e_sec) if n_sec > 0 else [] sec_pdgs = [pdgs[(row_idx + j) % 2] for j in range(n_sec)] sec_dirs = [_unit(rng.normal(size=3)) for _ in range(n_sec)] rows.append( @@ -211,9 +209,7 @@ def test_run_train_job_no_topn_map_for_physical_target(tmp_path, data): @pytest.mark.filterwarnings("ignore::DeprecationWarning:multiprocessing.popen_fork") -def test_run_train_job_warns_when_num_workers_exceeds_shared_quota( - tmp_path, data, monkeypatch -): +def test_run_train_job_warns_when_num_workers_exceeds_shared_quota(tmp_path, data, monkeypatch): # num_workers>0 makes DataLoader actually fork worker subprocesses # (unlike every other test here, which runs with num_workers=0) — pytest # itself is multi-threaded, hence Python's fork-safety warning below. @@ -223,9 +219,7 @@ def test_run_train_job_warns_when_num_workers_exceeds_shared_quota( @pytest.mark.filterwarnings("ignore::DeprecationWarning:multiprocessing.popen_fork") -def test_run_train_job_no_warning_when_num_workers_within_shared_quota( - tmp_path, data, monkeypatch -): +def test_run_train_job_no_warning_when_num_workers_within_shared_quota(tmp_path, data, monkeypatch): monkeypatch.setattr("giant.pipeline.os.cpu_count", lambda: 8) # quota = 2 echo = _run(data, tmp_path / "out", num_workers=2) assert not any("exceeds" in m for m in echo) @@ -299,12 +293,8 @@ def test_run_train_job_mixed_particle_material_conditioning_end_to_end(tmp_path, from giant.constants import COND_DIM_BASE, PARTICLE_PHYS_DIM assert cond_norm.mean is not None and cond_norm.std is not None - np.testing.assert_allclose( - cond_norm.mean[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 0.0 - ) - np.testing.assert_allclose( - cond_norm.std[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 1.0 - ) + np.testing.assert_allclose(cond_norm.mean[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 0.0) + np.testing.assert_allclose(cond_norm.std[COND_DIM_BASE : COND_DIM_BASE + PARTICLE_PHYS_DIM], 1.0) material_std = cond_norm.std[COND_DIM_BASE + PARTICLE_PHYS_DIM :] assert np.all(material_std > 0) and not np.allclose(material_std, 1.0) @@ -342,12 +332,8 @@ def test_run_train_job_matches_uncached_output(tmp_path, data): cached = torch.load(tmp_path / "cached2" / "last.pt", weights_only=False) for key in ("cond", "target", "sec_phys"): - np.testing.assert_allclose( - uncached["normalizer"][key]["mean"], cached["normalizer"][key]["mean"] - ) - np.testing.assert_allclose( - uncached["normalizer"][key]["std"], cached["normalizer"][key]["std"] - ) + np.testing.assert_allclose(uncached["normalizer"][key]["mean"], cached["normalizer"][key]["mean"]) + np.testing.assert_allclose(uncached["normalizer"][key]["std"], cached["normalizer"][key]["std"]) assert uncached["pdg_map"] == cached["pdg_map"] assert uncached["mat_map"] == cached["mat_map"] @@ -376,9 +362,7 @@ def test_seed_energy_router_falls_back_to_default_and_warns_when_no_samples(): router_cfg = {"enabled": True, "type": "energy", "n_experts": 4} cond_norm = _fitted_cond_norm() echoed = [] - _seed_energy_router( - router_cfg, cond_norm, np.empty(0), energy_idx=3, echo=echoed.append - ) + _seed_energy_router(router_cfg, cond_norm, np.empty(0), energy_idx=3, echo=echoed.append) assert "centers_init" not in router_cfg assert len(echoed) == 1 assert "falls back to default centers" in echoed[0] @@ -393,9 +377,7 @@ def test_seed_energy_router_seeds_centers_from_data_quantiles(): # units as the conditioning column being normalized against. energy_quantiles = np.linspace(1.0, 10.0, 33).astype(np.float32) echoed = [] - _seed_energy_router( - router_cfg, cond_norm, energy_quantiles, energy_idx, echoed.append - ) + _seed_energy_router(router_cfg, cond_norm, energy_quantiles, energy_idx, echoed.append) assert "centers_init" in router_cfg centers = np.asarray(router_cfg["centers_init"], dtype=np.float32) diff --git a/tests/test_render.py b/tests/test_render.py index f1ec9d5..064cb32 100644 --- a/tests/test_render.py +++ b/tests/test_render.py @@ -73,10 +73,7 @@ def test_render_router_diagnostics_and_edge_cases(tmp_path: Path): "x", { "edges": [0, 1, 2], - "groups": { - lbl: {"rollout": [1, 2], "reference": [2, 1]} - for lbl in ("a", "b", "c", "d") - }, + "groups": {lbl: {"rollout": [1, 2], "reference": [2, 1]} for lbl in ("a", "b", "c", "d")}, "log_y": True, }, ), @@ -126,9 +123,7 @@ def test_render_all_run_gallery_invokes_subprocess(tmp_path: Path, monkeypatch): for r in reduced: r.save(tmp_path / "reduced" / f"{r.id}.json") try: - render_mod.render_all( - tmp_path / "reduced", tmp_path / "plots", run_gallery=True - ) + render_mod.render_all(tmp_path / "reduced", tmp_path / "plots", run_gallery=True) except RuntimeError as e: pytest.skip(f"LaTeX rendering unavailable: {e}") @@ -145,9 +140,7 @@ def test_render_run_glues_condor_run_meta_into_render_all(tmp_path: Path, monkey (run_dir / "reduced").mkdir(parents=True) merge_calls = [] - monkeypatch.setattr( - condor_mod, "merge_all", lambda rd: merge_calls.append(Path(rd)) - ) + monkeypatch.setattr(condor_mod, "merge_all", lambda rd: merge_calls.append(Path(rd))) meta = condor_mod.RunMeta( rollout="rollout.parquet", reference="reference.parquet", @@ -157,9 +150,9 @@ def test_render_run_glues_condor_run_meta_into_render_all(tmp_path: Path, monkey ) monkeypatch.setattr(condor_mod.RunMeta, "load", classmethod(lambda cls, p: meta)) - Reduced( - "s", "species", "single_hist", "Single", "x", {"edges": [0, 1], "rollout": [1]} - ).save(run_dir / "reduced" / "s.json") + Reduced("s", "species", "single_hist", "Single", "x", {"edges": [0, 1], "rollout": [1]}).save( + run_dir / "reduced" / "s.json" + ) try: pdfs = render_mod.render_run(run_dir) @@ -363,9 +356,7 @@ def test_figure_params_old_shape_wgan_reports_noise_dim_not_steps(): def test_plot_metadata_includes_note_and_run_meta_parameters(): - r = Reduced( - "u", "router", "unavailable", "Unavailable", "x", {"note": "no router data"} - ) + r = Reduced("u", "router", "unavailable", "Unavailable", "x", {"note": "no router data"}) meta = render_mod._plot_metadata(r, {"title": "run-1", "checkpoint": "ckpt.pt"}) assert meta["note"] == "no router data" assert meta["parameters"] == {"checkpoint": "ckpt.pt"} diff --git a/tests/test_rollout.py b/tests/test_rollout.py index da8d624..1cdb799 100644 --- a/tests/test_rollout.py +++ b/tests/test_rollout.py @@ -121,12 +121,8 @@ def fake_material_props(monkeypatch): import giant.materials as gm fake = { - "G4_AIR": gm.MaterialProperties( - z_eff=7.3, a_eff=14.4, density=1.2e-3, x0=3.0e4, lambda_int=7.0e5 - ), - "G4_PbWO4": gm.MaterialProperties( - z_eff=75.6, a_eff=205.3, density=8.28, x0=0.89, lambda_int=20.7 - ), + "G4_AIR": gm.MaterialProperties(z_eff=7.3, a_eff=14.4, density=1.2e-3, x0=3.0e4, lambda_int=7.0e5), + "G4_PbWO4": gm.MaterialProperties(z_eff=75.6, a_eff=205.3, density=8.28, x0=0.89, lambda_int=20.7), } monkeypatch.setattr(gm, "MATERIAL_PROPERTIES", fake) return fake @@ -496,9 +492,7 @@ def test_resolve_n_sec_raises_when_neither_stage_owns_head(fake_material_props): @pytest.mark.parametrize("decoder", ["one_shot", "autoregressive"]) @pytest.mark.parametrize("generator2", ["flow", "wgan"]) -def test_rollout_physical_target_decoder_generator_matrix( - fake_material_props, decoder, generator2 -): +def test_rollout_physical_target_decoder_generator_matrix(fake_material_props, decoder, generator2): """Every (decoder, stage2 generator) combination under particle_type.target="physical" must run to completion and conserve energy.""" @@ -542,9 +536,7 @@ def test_rollout_onehot_target_end_to_end(fake_material_props, decoder): assert len(rec["event_id"]) > 0 # Every spawned secondary's nominal pdg must be one decode_topn_class can # actually produce (the topn map's known classes + its "other" members). - possible = set(PDG_TOPN_MAP.class_map.keys()) | set( - PDG_TOPN_MAP.other_members.keys() - ) + possible = set(PDG_TOPN_MAP.class_map.keys()) | set(PDG_TOPN_MAP.other_members.keys()) secondary_pdgs = set(rec["pdg"][rec["generation"] > 0].tolist()) assert secondary_pdgs <= possible @@ -589,9 +581,7 @@ def _onehot_conditioning_models(): return s1.eval(), s2.eval() -def _run_onehot_conditioning( - pdg_topn_map=COND_PDG_TOPN_MAP, mat_topn_map=COND_MAT_TOPN_MAP -): +def _run_onehot_conditioning(pdg_topn_map=COND_PDG_TOPN_MAP, mat_topn_map=COND_MAT_TOPN_MAP): s1, s2 = _onehot_conditioning_models() cond, tgt, sec_phys = _norms() return rollout( @@ -643,9 +633,7 @@ def test_rollout_embedding_target_end_to_end(decoder): conditioning's own particle embedding table, so every resolved PDG must be a real member of the dense training vocab (pdg_map) — unlike "onehot", there is no "other" bucket to fall outside of.""" - s1, s2 = _models_v3( - conditioning="embedding", decoder=decoder, target="embedding", emb_dim=4 - ) + s1, s2 = _models_v3(conditioning="embedding", decoder=decoder, target="embedding", emb_dim=4) rec = _run_v3(s1, s2, conditioning="embedding") assert len(rec["event_id"]) > 0 secondary_pdgs = set(rec["pdg"][rec["generation"] > 0].tolist()) diff --git a/tests/test_router.py b/tests/test_router.py index 01c0ded..d733218 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -25,9 +25,7 @@ MATERIAL_CFG = {"type": "physical", "emb_dim": 8, "n_layers": 1} def _cond(B=8, pdg=3, mat=2): cond_cont = torch.randn(B, COND_DIM) - cond_cat = torch.stack( - [torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1 - ) + cond_cat = torch.stack([torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1) return cond_cont, cond_cat @@ -78,9 +76,7 @@ def test_energy_router_gate_partition_of_unity(): def test_energy_router_top1_matches_gate_argmax(): router = EnergyRouter(n_experts=4) cond_cont, cond_cat = _cond(16) - assert torch.equal( - router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1) - ) + assert torch.equal(router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)) def test_energy_router_hardens_as_temperature_shrinks(): @@ -128,12 +124,8 @@ def test_energy_router_centers_init_wrong_length_raises(): def test_energy_router_centers_init_respects_learn_centers_flag(): - learned = EnergyRouter( - n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=True - ) - fixed = EnergyRouter( - n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=False - ) + learned = EnergyRouter(n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=True) + fixed = EnergyRouter(n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=False) assert isinstance(learned.centers, torch.nn.Parameter) assert not isinstance(fixed.centers, torch.nn.Parameter) @@ -160,9 +152,7 @@ def test_energy_router_learn_width_matches_fixed_temperature_at_init(): per-expert width must reproduce the fixed-temperature gate exactly.""" centers_init = [-1.0, 0.0, 0.5, 1.5] fixed = EnergyRouter(n_experts=4, temperature=0.3, centers_init=centers_init) - learned = EnergyRouter( - n_experts=4, temperature=0.3, centers_init=centers_init, learn_width=True - ) + learned = EnergyRouter(n_experts=4, temperature=0.3, centers_init=centers_init, learn_width=True) cond_cont, cond_cat = _cond(16) torch.testing.assert_close( learned.gate(cond_cont, cond_cat), @@ -195,21 +185,15 @@ def test_energy_router_learn_width_and_temperature_mutually_exclusive_raises(): EnergyRouter(n_experts=4, learn_width=True, learn_temperature=True) except ValueError: return - raise AssertionError( - "expected ValueError for learn_width and learn_temperature both set" - ) + raise AssertionError("expected ValueError for learn_width and learn_temperature both set") def test_energy_router_width_ratio_bounds_must_bracket_one_raises(): try: - EnergyRouter( - n_experts=4, learn_width=True, width_min_ratio=1.0, width_max_ratio=2.0 - ) + EnergyRouter(n_experts=4, learn_width=True, width_min_ratio=1.0, width_max_ratio=2.0) except ValueError: return - raise AssertionError( - "expected ValueError for width_min_ratio/width_max_ratio not bracketing 1.0" - ) + raise AssertionError("expected ValueError for width_min_ratio/width_max_ratio not bracketing 1.0") def test_energy_router_effective_width_stays_within_bounds(): @@ -246,9 +230,7 @@ def test_energy_router_learn_width_hardens_when_pushed_to_floor(): """Pushing every expert's width toward the (tiny) floor should harden the gate to a one-hot at the nearest center, generalizing the fixed- temperature->0 hardening test to the per-expert path.""" - router = EnergyRouter( - n_experts=4, learn_width=True, width_min_ratio=1e-4, width_max_ratio=10.0 - ) + router = EnergyRouter(n_experts=4, learn_width=True, width_min_ratio=1e-4, width_max_ratio=10.0) with torch.no_grad(): router.raw_width.fill_(-1e6) cond_cont, cond_cat = _cond(16) @@ -264,9 +246,7 @@ def test_energy_router_own_width_controls_own_coverage_independent_of_others(): expert's own gate share, without needing to touch any other expert's width — the "each expert learns its own coverage independently" property this feature is meant to add.""" - router = EnergyRouter( - n_experts=2, temperature=1.0, learn_width=True, centers_init=[0.0, 10.0] - ) + router = EnergyRouter(n_experts=2, temperature=1.0, learn_width=True, centers_init=[0.0, 10.0]) cond_cont, cond_cat = _cond(4) cond_cont[:, 3] = 3.0 # fixed energy, unequal distance to each center @@ -280,9 +260,7 @@ def test_energy_router_own_width_controls_own_coverage_independent_of_others(): def test_build_router_threads_learn_width_kwargs_through(): - router = build_router( - "energy", 4, learn_width=True, width_min_ratio=0.2, width_max_ratio=8.0 - ) + router = build_router("energy", 4, learn_width=True, width_min_ratio=0.2, width_max_ratio=8.0) assert isinstance(router, EnergyRouter) assert router.learn_width is True assert isinstance(router.raw_width, torch.nn.Parameter) @@ -419,9 +397,7 @@ def test_pdg_router_gate_partition_of_unity(): def test_pdg_router_top1_matches_gate_argmax(): router = PdgRouter(n_experts=4, pdg_vocab=3) cond_cont, cond_cat = _cond(16) - assert torch.equal( - router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1) - ) + assert torch.equal(router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)) def test_pdg_router_hardens_as_temperature_shrinks(): @@ -586,9 +562,7 @@ def test_process_router_gate_partition_of_unity(): def test_process_router_top1_matches_gate_argmax(): router = ProcessRouter(n_experts=4, pdg_vocab=3, mat_vocab=2) cond_cont, cond_cat = _cond(16) - assert torch.equal( - router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1) - ) + assert torch.equal(router.top1(cond_cont, cond_cat), router.gate(cond_cont, cond_cat).argmax(-1)) def test_process_router_balance_loss_is_nonnegative_scalar(): @@ -663,16 +637,12 @@ def test_build_models_routed_with_process_router(): def test_composed_router_n_experts_is_product(): - router = ComposedRouter( - [EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)] - ) + router = ComposedRouter([EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]) assert router.n_experts == 12 def test_composed_router_gate_partition_of_unity(): - router = ComposedRouter( - [EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)] - ) + router = ComposedRouter([EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]) cond_cont, cond_cat = _cond(16, pdg=5) g = router.gate(cond_cont, cond_cat) assert g.shape == (16, 12) @@ -709,9 +679,7 @@ def test_composed_router_top1_factors_into_per_axis_argmax(): def test_composed_router_supports_different_expert_counts_per_axis(): - router = ComposedRouter( - [EnergyRouter(n_experts=5), PdgRouter(n_experts=2, pdg_vocab=5)] - ) + router = ComposedRouter([EnergyRouter(n_experts=5), PdgRouter(n_experts=2, pdg_vocab=5)]) assert router.n_experts == 10 cond_cont, cond_cat = _cond(8, pdg=5) assert router.gate(cond_cont, cond_cat).shape == (8, 10) @@ -719,9 +687,7 @@ def test_composed_router_supports_different_expert_counts_per_axis(): def test_composed_router_classify_loss_sums_sub_router_losses(): """energy/pdg both default to zero, so the composed loss should too.""" - router = ComposedRouter( - [EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)] - ) + router = ComposedRouter([EnergyRouter(n_experts=4), PdgRouter(n_experts=3, pdg_vocab=5)]) cond_cont, cond_cat = _cond(16, pdg=5) labels = torch.randint(0, 4, (16,)) loss = router.classify_loss(cond_cont, cond_cat, labels) @@ -964,9 +930,7 @@ def test_routed_stage1_eval_dispatch_matches_manual_grouping(): idx = model.trunk.router.top1(cond_cont, cond_cat) manual = torch.zeros_like(x_t) for i in range(B): - manual[i] = model.trunk.experts[int(idx[i])]( - x_t[i : i + 1], cond[i : i + 1] - )[0] + manual[i] = model.trunk.experts[int(idx[i])](x_t[i : i + 1], cond[i : i + 1])[0] torch.testing.assert_close(batched, manual, atol=1e-5, rtol=1e-4) diff --git a/tests/test_sample.py b/tests/test_sample.py index f6b12e7..7906069 100644 --- a/tests/test_sample.py +++ b/tests/test_sample.py @@ -30,9 +30,7 @@ def _particle_material_cfg(conditioning: str, emb_dim: int) -> tuple[dict, dict] def _cond(B: int, pdg: int = 3, mat: int = 2) -> tuple[torch.Tensor, torch.Tensor]: cond_cont = torch.randn(B, COND_DIM) - cond_cat = torch.stack( - [torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1 - ) + cond_cat = torch.stack([torch.randint(0, pdg, (B,)), torch.randint(0, mat, (B,))], dim=1) return cond_cont, cond_cat @@ -43,12 +41,8 @@ def _conditioning_for(target: str) -> str: return "embedding" if target == "embedding" else "physical" -def _stage2_oneshot( - target: str, generator: str, emb_dim: int = 6, pdg: int = 3, mat: int = 2 -) -> Stage2OneShot: - particle_cfg, material_cfg = _particle_material_cfg( - _conditioning_for(target), emb_dim - ) +def _stage2_oneshot(target: str, generator: str, emb_dim: int = 6, pdg: int = 3, mat: int = 2) -> Stage2OneShot: + particle_cfg, material_cfg = _particle_material_cfg(_conditioning_for(target), emb_dim) particle_type_cfg = {"target": target} # build_models (giant/model/network.py) computes sec_dim this same way # before constructing Stage2OneShot — its own default (SEC_DIM, the @@ -78,9 +72,7 @@ def _stage2_ar( k_max: int = 5, history: str = "markov", ) -> Stage2Autoregressive: - particle_cfg, material_cfg = _particle_material_cfg( - _conditioning_for(target), emb_dim - ) + particle_cfg, material_cfg = _particle_material_cfg(_conditioning_for(target), emb_dim) return Stage2Autoregressive( pdg_vocab=pdg, mat_vocab=mat, @@ -163,9 +155,7 @@ def test_sample_secondaries_flow_shapes_by_target(target): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, K_MAX + 1, (B,)) - sec_cont, sec_type, sec_valid = sample_secondaries( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 - ) + sec_cont, sec_type, sec_valid = sample_secondaries(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2) assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM) assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim)) assert sec_valid.shape == (B, K_MAX) @@ -180,9 +170,7 @@ def test_sample_secondaries_wgan_shapes_by_target(target): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, K_MAX + 1, (B,)) - sec_cont, sec_type, sec_valid = sample_secondaries_wgan( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred - ) + sec_cont, sec_type, sec_valid = sample_secondaries_wgan(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred) assert sec_cont.shape == (B, K_MAX, CONT_SLOT_DIM) assert sec_type.shape == (B, K_MAX, _expected_type_dim(target, emb_dim)) assert sec_valid.shape == (B, K_MAX) @@ -196,15 +184,11 @@ def test_sample_secondaries_wgan_shapes_by_target(target): @pytest.mark.parametrize("target", ["physical", "onehot", "embedding"]) def test_sample_secondaries_ar_shapes(target, generator, history): B, k_max, emb_dim = 4, 5, 6 - decoder = _stage2_ar( - target, generator, emb_dim=emb_dim, k_max=k_max, history=history - ) + decoder = _stage2_ar(target, generator, emb_dim=emb_dim, k_max=k_max, history=history) cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, k_max + 1, (B,)) - sec_cont, sec_type, sec_valid = sample_secondaries_ar( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 - ) + sec_cont, sec_type, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2) assert sec_cont.shape == (B, k_max, CONT_SLOT_DIM) assert sec_type.shape == (B, k_max, _expected_type_dim(target, emb_dim)) assert sec_valid.shape == (B, k_max) @@ -220,9 +204,7 @@ def test_sample_secondaries_ar_valid_mask_matches_n_sec(target, generator): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.tensor([0, 2, k_max]) - _, _, sec_valid = sample_secondaries_ar( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 - ) + _, _, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2) for i, n in enumerate(n_sec_pred.tolist()): assert sec_valid[i, :n].all() assert not sec_valid[i, n:].any() @@ -237,8 +219,6 @@ def test_sample_secondaries_ar_first_slot_has_no_history(): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.tensor([0, 1, 1]) - sec_cont, sec_type, sec_valid = sample_secondaries_ar( - decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2 - ) + sec_cont, sec_type, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2) assert sec_cont.shape == (B, 1, CONT_SLOT_DIM) assert sec_valid.tolist() == [[False], [True], [True]] diff --git a/tests/test_setup_cache.py b/tests/test_setup_cache.py index 70e8e40..584b5e8 100644 --- a/tests/test_setup_cache.py +++ b/tests/test_setup_cache.py @@ -14,9 +14,7 @@ from giant.data.transforms import Normalizer def _touch_parquet(path, n=1): path.parent.mkdir(parents=True, exist_ok=True) - pd.DataFrame( - {"pdg": [11] * n, "material": ["G4_AIR"] * n, "process": ["eIoni"] * n} - ).to_parquet(path) + pd.DataFrame({"pdg": [11] * n, "material": ["G4_AIR"] * n, "process": ["eIoni"] * n}).to_parquet(path) return path @@ -29,9 +27,7 @@ def _normalizer(width=3): def _entry(n_train_steps=100, sample=None): sample = np.array([1.0, 2.0, 3.0], dtype=np.float32) if sample is None else sample - return NormalizerEntry( - _normalizer(), _normalizer(), _normalizer(2), n_train_steps, sample - ) + return NormalizerEntry(_normalizer(), _normalizer(), _normalizer(2), n_train_steps, sample) # ── sidecar_path ───────────────────────────────────────────────────────── @@ -39,9 +35,7 @@ def _entry(n_train_steps=100, sample=None): def test_sidecar_path_single_file(tmp_path): f = tmp_path / "shard.parquet" - assert ( - setup_cache.sidecar_path(f) == tmp_path / "shard.parquet.giant_train_cache.json" - ) + assert setup_cache.sidecar_path(f) == tmp_path / "shard.parquet.giant_train_cache.json" def test_sidecar_path_directory(tmp_path): @@ -51,10 +45,7 @@ def test_sidecar_path_directory(tmp_path): def test_sidecar_path_manifest(tmp_path): m = tmp_path / "pools" / "full.manifest" - assert ( - setup_cache.sidecar_path(m) - == tmp_path / "pools" / "full.manifest.giant_train_cache.json" - ) + assert setup_cache.sidecar_path(m) == tmp_path / "pools" / "full.manifest.giant_train_cache.json" # ── fingerprint_files ──────────────────────────────────────────────────── diff --git a/tests/test_steps_to_parquet.py b/tests/test_steps_to_parquet.py index 8e839c1..169bda8 100644 --- a/tests/test_steps_to_parquet.py +++ b/tests/test_steps_to_parquet.py @@ -24,18 +24,14 @@ def _frame() -> pl.DataFrame: def test_e_sec_sums_child_first_step_energy(): out, n_orphaned = steps_to_parquet._add_secondary_attributes(_frame()) assert n_orphaned == 0 - e_sec = dict( - zip(zip(out["track_id"], out["step_no"], out["event_id"]), out["e_sec"]) - ) + e_sec = dict(zip(zip(out["track_id"], out["step_no"], out["event_id"]), out["e_sec"])) assert e_sec[(1, 0, 0)] == 15.0 # one child, first-step pre_E 15 assert e_sec[(1, 0, 1)] == 50.0 # two children, 20 + 30 def test_e_sec_zero_when_no_children(): out, _ = steps_to_parquet._add_secondary_attributes(_frame()) - childless = out.filter( - (pl.col("event_id") == 0) & (pl.col("track_id") == 1) & (pl.col("step_no") == 1) - ) + childless = out.filter((pl.col("event_id") == 0) & (pl.col("track_id") == 1) & (pl.col("step_no") == 1)) assert childless["e_sec"].item() == 0.0 @@ -69,9 +65,7 @@ def test_orphaned_child_track_is_dropped_not_nulled(): } ) out, n_orphaned = steps_to_parquet._add_secondary_attributes(df) - row = out.filter( - (pl.col("event_id") == 0) & (pl.col("track_id") == 1) & (pl.col("step_no") == 0) - ) + row = out.filter((pl.col("event_id") == 0) & (pl.col("track_id") == 1) & (pl.col("step_no") == 0)) assert n_orphaned == 1 assert row["child_track_ids"].to_list() == [[2]] diff --git a/tests/test_steps_to_parquet_parallel.py b/tests/test_steps_to_parquet_parallel.py index 55e0cce..101d36f 100644 --- a/tests/test_steps_to_parquet_parallel.py +++ b/tests/test_steps_to_parquet_parallel.py @@ -124,31 +124,13 @@ def _make_dataset(tmp_path: Path, schemas: list[str] | None = None) -> Path: def test_resolve_destination_uses_latest_schema(tmp_path): root_file = _make_dataset(tmp_path, schemas=["schema1", "schema3", "schema2"]) dest = resolve_destination(root_file, tmp_path, schema_override=None) - assert ( - dest - == tmp_path - / "processed" - / "steps" - / "gen1" - / "schema3" - / "pbwo4" - / "shard-000.parquet" - ) + assert dest == tmp_path / "processed" / "steps" / "gen1" / "schema3" / "pbwo4" / "shard-000.parquet" def test_resolve_destination_schema_override_wins(tmp_path): root_file = _make_dataset(tmp_path, schemas=["schema1", "schema3"]) dest = resolve_destination(root_file, tmp_path, schema_override="schema9") - assert ( - dest - == tmp_path - / "processed" - / "steps" - / "gen1" - / "schema9" - / "pbwo4" - / "shard-000.parquet" - ) + assert dest == tmp_path / "processed" / "steps" / "gen1" / "schema9" / "pbwo4" / "shard-000.parquet" def test_resolve_destination_errors_without_any_schema(tmp_path): diff --git a/tests/test_train.py b/tests/test_train.py index ad14113..9a220fe 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -74,9 +74,7 @@ def test_wandb_run_config_includes_full_cfg_and_param_counts(): "stage1_model": {"generator": "flow"}, "stage2_model": {"generator": "wgan"}, } - wcfg = _wandb_run_config( - cfg, model_config={"pdg_vocab": 3}, param_counts={"stage1": 100} - ) + wcfg = _wandb_run_config(cfg, model_config={"pdg_vocab": 3}, param_counts={"stage1": 100}) assert wcfg["train"] == {"lr": 3e-4} assert wcfg["stage1_model"] == {"generator": "flow"} assert wcfg["stage2_model"] == {"generator": "wgan"} @@ -162,9 +160,7 @@ def test_type_repr_shapes_and_values(target): expected_width = PARTICLE_PHYS_DIM if target == "physical" else emb_dim assert repr_.shape == (B, K, expected_width) if target == "physical": - assert torch.equal( - repr_, sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM] - ) + assert torch.equal(repr_, sec_cont[..., CONT_SLOT_DIM : CONT_SLOT_DIM + PARTICLE_PHYS_DIM]) if target == "onehot": assert torch.all(repr_.sum(-1) == 1.0) @@ -180,9 +176,7 @@ def test_type_repr_shapes_and_values(target): ("embedding", "wgan"), ], ) -def test_assemble_stage2_ar_target_matches_assemble_stage2_real_flattened( - target, generator -): +def test_assemble_stage2_ar_target_matches_assemble_stage2_real_flattened(target, generator): """Regression test tying the refactor together: _assemble_stage2_real is now defined as _assemble_stage2_ar_target(...).flatten(1).""" B, emb_dim = 4, 6 @@ -192,12 +186,8 @@ def test_assemble_stage2_ar_target_matches_assemble_stage2_real_flattened( if target == "embedding": cond_enc.pdg_emb = torch.nn.Embedding(emb_dim, emb_dim) particle_type_cfg = {"target": target} - flat = _assemble_stage2_real( - sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim - ) - unflat = _assemble_stage2_ar_target( - sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim - ) + flat = _assemble_stage2_real(sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim) + unflat = _assemble_stage2_ar_target(sec_cont, sec_type_idx, particle_type_cfg, generator, cond_enc, emb_dim) assert torch.equal(unflat.flatten(1), flat) @@ -206,9 +196,7 @@ def test_assemble_stage2_ar_inputs_shapes_and_history_feat_width(): sec_cont = torch.randn(B, K_MAX, SEC_SLOT_DIM) sec_type_idx = torch.randint(0, emb_dim, (B, K_MAX)) cond_enc = torch.nn.Module() - out = _assemble_stage2_ar_inputs( - sec_cont, sec_type_idx, {"target": "physical"}, cond_enc, emb_dim - ) + out = _assemble_stage2_ar_inputs(sec_cont, sec_type_idx, {"target": "physical"}, cond_enc, emb_dim) assert out["history_feat"].shape == (B, K_MAX, CONT_SLOT_DIM + PARTICLE_PHYS_DIM) assert out["has_prev"].shape == (B, K_MAX) assert out["remaining_frac"].shape == (B, K_MAX) @@ -221,9 +209,7 @@ def test_relax_onehot_type_slice_grad_probe_populates_both_norms(): B, k_max, cont_dim, type_dim = 4, K_MAX, CONT_SLOT_DIM, 6 x_flat = torch.randn(B, k_max * (cont_dim + type_dim), requires_grad=True) grad_probe: dict[str, float] = {} - out = _relax_onehot_type_slice( - x_flat, k_max, cont_dim, type_dim, tau=0.5, grad_probe=grad_probe - ) + out = _relax_onehot_type_slice(x_flat, k_max, cont_dim, type_dim, tau=0.5, grad_probe=grad_probe) out.sum().backward() assert grad_probe["cont"] >= 0.0 assert grad_probe["type"] >= 0.0 @@ -324,9 +310,7 @@ def _fake_batches(n_batches, batch_size, seed=0): sec_cont = torch.randn(batch_size, K_MAX, SEC_SLOT_DIM, generator=g) proc_idx = torch.zeros(batch_size, dtype=torch.long) sec_type_idx = torch.zeros(batch_size, K_MAX, dtype=torch.long) - batches.append( - (cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx) - ) + batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx)) return batches @@ -409,17 +393,13 @@ def _run_train(cfg, out_dir, resume_path=None): ), ( "stage2_onehot_target_wgan", - lambda cfg: cfg["stage2_model"].__setitem__( - "particle_type", {"target": "onehot", "lambda": 1.0} - ), + lambda cfg: cfg["stage2_model"].__setitem__("particle_type", {"target": "onehot", "lambda": 1.0}), ), ( "stage2_onehot_target_flow", lambda cfg: ( cfg["stage2_model"].__setitem__("generator", "flow"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "onehot", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "onehot", "lambda": 1.0}), ), ), ( @@ -427,9 +407,7 @@ def _run_train(cfg, out_dir, resume_path=None): lambda cfg: ( cfg["conditioning"]["particle"].__setitem__("type", "embedding"), cfg["conditioning"]["material"].__setitem__("type", "embedding"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "embedding", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "embedding", "lambda": 1.0}), ), ), ( @@ -438,18 +416,14 @@ def _run_train(cfg, out_dir, resume_path=None): cfg["conditioning"]["particle"].__setitem__("type", "embedding"), cfg["conditioning"]["material"].__setitem__("type", "embedding"), cfg["stage2_model"].__setitem__("generator", "flow"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "embedding", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "embedding", "lambda": 1.0}), ), ), ( "ar_wgan_onehot", lambda cfg: ( cfg["stage2_model"].__setitem__("decoder", "autoregressive"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "onehot", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "onehot", "lambda": 1.0}), ), ), ( @@ -461,9 +435,7 @@ def _run_train(cfg, out_dir, resume_path=None): lambda cfg: ( cfg["stage2_model"].__setitem__("decoder", "autoregressive"), cfg["stage2_model"].__setitem__("generator", "flow"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "onehot", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "onehot", "lambda": 1.0}), ), ), ( @@ -473,9 +445,7 @@ def _run_train(cfg, out_dir, resume_path=None): cfg["conditioning"]["material"].__setitem__("type", "embedding"), cfg["stage2_model"].__setitem__("decoder", "autoregressive"), cfg["stage2_model"].__setitem__("generator", "flow"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "embedding", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "embedding", "lambda": 1.0}), ), ), ( @@ -491,9 +461,7 @@ def _run_train(cfg, out_dir, resume_path=None): cfg["stage1_model"].__setitem__("generator", "wgan"), cfg["stage2_model"].__setitem__("generator", "flow"), cfg["stage2_model"].__setitem__("decoder", "autoregressive"), - cfg["stage2_model"].__setitem__( - "particle_type", {"target": "onehot", "lambda": 1.0} - ), + cfg["stage2_model"].__setitem__("particle_type", {"target": "onehot", "lambda": 1.0}), ), ), ], @@ -585,9 +553,7 @@ def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): ema_decay=0.0, steps_per_epoch=4, ) - trainer = WGANStageTrainer( - spec, models["stage1"], critics["stage1"], torch.device("cpu") - ) + trainer = WGANStageTrainer(spec, models["stage1"], critics["stage1"], torch.device("cpu")) assert trainer.model.n_sec_head is None batch = _fake_batches(1, 8)[0] stats = trainer.step(batch, torch.device("cpu"), global_step=1) @@ -595,9 +561,7 @@ def test_wgan_stage_trainer_skips_generator_step_when_no_grad_this_batch(): def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): - spec = StageSpec( - name="stage2", is_stage2=True, generator="ddpm", ddpm_n_steps=50, ema_decay=0.0 - ) + spec = StageSpec(name="stage2", is_stage2=True, generator="ddpm", ddpm_n_steps=50, ema_decay=0.0) with pytest.raises(NotImplementedError): FlowDDPMStageTrainer(spec, torch.nn.Linear(1, 1), torch.device("cpu")) @@ -608,9 +572,7 @@ def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): @pytest.mark.parametrize("teacher_forcing", ["always", "scheduled", "never"]) @pytest.mark.parametrize("history", ["markov", "attention"]) @pytest.mark.parametrize("stage2_generator", ["wgan", "flow"]) -def test_build_stage_trainers_ar_scheduled_and_attention_step_runs( - teacher_forcing, history, stage2_generator -): +def test_build_stage_trainers_ar_scheduled_and_attention_step_runs(teacher_forcing, history, stage2_generator): """v0.3.0 step 7: history='attention' and teacher_forcing in {'scheduled', 'never'} must actually train — a stage-2 AR trainer.step() must run and produce a finite loss, for every {history} x @@ -629,9 +591,7 @@ def test_build_stage_trainers_ar_scheduled_and_attention_step_runs( model_config = _model_config(cfg) models = build_models(model_config) critics = build_critics(model_config) - trainers = build_stage_trainers( - cfg, models, critics, torch.device("cpu"), total_train_batches=4 - ) + trainers = build_stage_trainers(cfg, models, critics, torch.device("cpu"), total_train_batches=4) trainer = trainers["stage2"] batch = _fake_batches(1, 4)[0] stats = trainer.step(batch, torch.device("cpu"), global_step=1) @@ -665,9 +625,7 @@ def test_train_end_to_end_ar_attention_history_scheduled_teacher_forcing( with open(out_dir / "metrics.csv", newline="") as f: rows = list(csv.DictReader(f)) assert len(rows) == cfg["train"]["epochs"] - loss_col = ( - "stage2/train/g_loss" if stage2_generator == "wgan" else "stage2/train/loss" - ) + loss_col = "stage2/train/g_loss" if stage2_generator == "wgan" else "stage2/train/loss" assert all(math.isfinite(float(r[loss_col])) for r in rows) diff --git a/tests/test_transforms.py b/tests/test_transforms.py index 04ce58d..7f19326 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -162,9 +162,7 @@ def test_local_frame_rotation_normalizes_non_unit_pre_dir(): post_dir = rng.standard_normal((N, 3)).astype(np.float32) post_dir /= np.linalg.norm(post_dir, axis=1, keepdims=True) - pre_dir_scaled = pre_dir_unit * rng.uniform(0.9, 1.1, size=(N, 1)).astype( - np.float32 - ) + pre_dir_scaled = pre_dir_unit * rng.uniform(0.9, 1.1, size=(N, 1)).astype(np.float32) expected = local_frame_rotation(pre_dir_unit, post_dir) result = local_frame_rotation(pre_dir_scaled, post_dir) np.testing.assert_allclose(result, expected, atol=1e-4) @@ -200,14 +198,10 @@ def test_reconstruct_post_pos_straight_line(): step_length = rng.uniform(0.1, 5.0, size=N).astype(np.float32) post_pos = pre_pos + step_length[:, None] * pre_dir - travel_dir_local = local_frame_rotation( - pre_dir, travel_direction(pre_pos, post_pos) - ) + travel_dir_local = local_frame_rotation(pre_dir, travel_direction(pre_pos, post_pos)) np.testing.assert_allclose(travel_dir_local, np.tile([0, 0, 1], (N, 1)), atol=1e-4) - reconstructed = reconstruct_post_pos( - pre_pos, pre_dir, step_length, travel_dir_local - ) + reconstructed = reconstruct_post_pos(pre_pos, pre_dir, step_length, travel_dir_local) np.testing.assert_allclose(reconstructed, post_pos, atol=1e-4) @@ -221,12 +215,8 @@ def test_reconstruct_post_pos_general_roundtrip(): post_pos = pre_pos + rng.standard_normal((N, 3)).astype(np.float32) step_length = np.linalg.norm(post_pos - pre_pos, axis=1).astype(np.float32) - travel_dir_local = local_frame_rotation( - pre_dir, travel_direction(pre_pos, post_pos) - ) - reconstructed = reconstruct_post_pos( - pre_pos, pre_dir, step_length, travel_dir_local - ) + travel_dir_local = local_frame_rotation(pre_dir, travel_direction(pre_pos, post_pos)) + reconstructed = reconstruct_post_pos(pre_pos, pre_dir, step_length, travel_dir_local) np.testing.assert_allclose(reconstructed, post_pos, atol=1e-4) @@ -356,9 +346,7 @@ def _step_data_no_sec_lists(n_sec: np.ndarray) -> dict: def test_build_features_proc_idx_zero_without_proc_map(): - data = _minimal_step_data( - 3, process=np.array(["compt", "phot", "eIoni"], dtype=object) - ) + data = _minimal_step_data(3, process=np.array(["compt", "phot", "eIoni"], dtype=object)) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} *_, proc_idx, _, _, _ = build_features(data, pdg_map, mat_map) @@ -367,9 +355,7 @@ def test_build_features_proc_idx_zero_without_proc_map(): def test_build_features_proc_idx_looks_up_proc_map(): - data = _minimal_step_data( - 3, process=np.array(["compt", "phot", "eIoni"], dtype=object) - ) + data = _minimal_step_data(3, process=np.array(["compt", "phot", "eIoni"], dtype=object)) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} proc_map = {"compt": 0, "phot": 1, "eIoni": 2} @@ -396,9 +382,7 @@ def test_build_features_require_secondaries_ok_when_no_secondaries(): data = _step_data_no_sec_lists(np.zeros(3, dtype=np.int32)) pdg_map, mat_map = {11: 0}, {"PbWO4": 0} - _, _, _, _, sec_cont, *_ = build_features( - data, pdg_map, mat_map, require_secondaries=True - ) + _, _, _, _, sec_cont, *_ = build_features(data, pdg_map, mat_map, require_secondaries=True) assert not sec_cont.any() @@ -413,11 +397,7 @@ def fake_material_props(monkeypatch): in by the user (see giant.materials.MaterialPropertiesNotFilledError).""" import giant.materials as gm - fake = { - "PbWO4": gm.MaterialProperties( - z_eff=75.6, a_eff=205.3, density=8.28, x0=0.89, lambda_int=20.7 - ) - } + fake = {"PbWO4": gm.MaterialProperties(z_eff=75.6, a_eff=205.3, density=8.28, x0=0.89, lambda_int=20.7)} monkeypatch.setattr(gm, "MATERIAL_PROPERTIES", fake) return fake @@ -455,9 +435,7 @@ def test_build_features_physical_mode_shape_and_values(fake_material_props): assert cond_cont.shape[1] == COND_DIM mass, charge = particle_mass_charge(11) expected_log_mass = log_transform(np.array([mass]))[0] - np.testing.assert_allclose( - cond_cont[:, COND_DIM_BASE], expected_log_mass, atol=1e-5 - ) + np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE], expected_log_mass, atol=1e-5) np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 1], charge) np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 2], 75.6) # z_eff np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 3], 205.3) # a_eff @@ -500,9 +478,7 @@ def test_build_cond_features_mass_charge_override(fake_material_props): material_conditioning="physical", ) - np.testing.assert_allclose( - cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0])) - ) + np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0]))) np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 1], [2.0, -2.0]) @@ -714,9 +690,7 @@ def test_welford_accumulator_matches_naive_running_mean_reference(): naive_M2 = np.zeros(F) naive_n = 0 for chunk in chunks: - naive_mean, naive_M2, naive_n = naive_update( - naive_mean, naive_M2, naive_n, chunk - ) + naive_mean, naive_M2, naive_n = naive_update(naive_mean, naive_M2, naive_n, chunk) acc = _WelfordAccumulator(F) for chunk in chunks: diff --git a/tests/test_validate.py b/tests/test_validate.py index 1e29517..ca220ae 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -57,9 +57,7 @@ def _loader(B: int = 4, n_batches: int = 2, n_sec_value: int = 0, n_classes: int sec_cont = torch.randn(B, _K_MAX, SEC_SLOT_DIM) proc_idx = torch.zeros(B, dtype=torch.long) sec_type_idx = torch.randint(0, n_classes, (B, _K_MAX), dtype=torch.long) - batches.append( - (cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx) - ) + batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx)) return batches @@ -71,9 +69,7 @@ def test_validate_marginals_all_zero_secondaries_returns_nan_phys_kl(monkeypatch s1, s2 = _tiny_models() loader = _loader(n_sec_value=0) - def _fake_resolve_n_sec( - stage1_model, sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred - ): + def _fake_resolve_n_sec(stage1_model, sec_decoder, cond_cont, cond_cat, stage1_out, n_sec_pred): return torch.zeros(cond_cont.size(0), dtype=torch.long) monkeypatch.setattr("giant.validate.resolve_n_sec", _fake_resolve_n_sec) diff --git a/tests/test_wgan.py b/tests/test_wgan.py index 851527b..5b17aa8 100644 --- a/tests/test_wgan.py +++ b/tests/test_wgan.py @@ -152,9 +152,7 @@ def test_sample_secondaries_wgan_shape(): cond_cont, cond_cat = _cond(B) stage1_out = torch.randn(B, X_DIM) n_sec_pred = torch.randint(0, K_MAX, (B,)) - sec_cont, sec_phys, sec_valid = sample_secondaries_wgan( - model, cond_cont, cond_cat, stage1_out, n_sec_pred - ) + sec_cont, sec_phys, sec_valid = sample_secondaries_wgan(model, cond_cont, cond_cat, stage1_out, n_sec_pred) assert sec_cont.shape == (B, K_MAX, 4) assert sec_phys.shape == (B, K_MAX, 2) assert sec_valid.shape == (B, K_MAX) @@ -183,9 +181,7 @@ def test_gradient_penalty_masked(): mask = _mask(B, n_sec) real = torch.randn(B, SEC_DIM) * mask fake = torch.randn(B, SEC_DIM) * mask - gp = gradient_penalty( - lambda x: sec_critic(x, cond_cont, cond_cat, stage1_out), real, fake, mask=mask - ) + gp = gradient_penalty(lambda x: sec_critic(x, cond_cont, cond_cat, stage1_out), real, fake, mask=mask) assert gp.item() >= 0.0 @@ -195,9 +191,7 @@ def test_critic_loss_scalar_and_grad(): cond_cont, cond_cat = _cond(B) real = torch.randn(B, X_DIM) fake = torch.randn(B, X_DIM) - loss = critic_loss( - lambda x: critic(x, cond_cont, cond_cat), real, fake.detach(), gp_weight=10.0 - ) + loss = critic_loss(lambda x: critic(x, cond_cont, cond_cat), real, fake.detach(), gp_weight=10.0) assert loss.shape == () loss.backward() assert any(p.grad is not None for p in critic.parameters()) -- 2.39.5 From 9bf5874308977368c2d2f35e241e714e76e9db60 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 14:35:14 +0200 Subject: [PATCH 25/30] Make config dataclasses the single source of truth for DEFAULT_CONFIG DEFAULT_CONFIG and build_models/build_critics/StageSpec.from_config's inline .get(key, default) fallbacks had already drifted: two keys (stage2_model.decoder, stage2_model.particle_type.target) resolved differently depending on whether a config dict came from merge_cli_overrides (fully populated, correct) or was hand-built and partial (fell back to stale v0.2-shaped literals). Introduce frozen dataclasses (GiantConfig and its nested blocks) in giant/config.py as the actual single declaration of every default; DEFAULT_CONFIG is now generated from them instead of hand-maintained, and build_models, build_critics, and StageSpec.from_config consume the dataclasses instead of duplicating literal fallbacks, so this class of drift can't recur. Router/n_sec sub-blocks keep an `extra` catch-all for their genuinely dynamic keys (composed-router axes, runtime-seeded centers_init, legacy_owner). Fixing the fallback surfaced the same latent bug in two existing partial-config callers that had been silently depending on it: a test fixture in test_train.py and scripts/warm_setup_cache.py's minimal cfg (now merged against DEFAULT_CONFIG instead of hand-rolled, closing the gap for good). See issues.md Issue 1. Co-Authored-By: Claude Sonnet 5 --- giant/config.py | 952 ++++++++++++++++++++++++++---------- giant/model/network.py | 120 ++--- giant/pipeline.py | 4 +- giant/training/trainers.py | 77 +-- scripts/warm_setup_cache.py | 27 +- tests/test_config.py | 74 +++ tests/test_network.py | 56 +++ tests/test_train.py | 22 +- 8 files changed, 963 insertions(+), 369 deletions(-) diff --git a/giant/config.py b/giant/config.py index 92c42d9..66d8cd7 100644 --- a/giant/config.py +++ b/giant/config.py @@ -4,6 +4,7 @@ import random import subprocess import sys import tomllib +from dataclasses import dataclass, field from datetime import datetime, timezone from enum import Enum from pathlib import Path @@ -29,272 +30,691 @@ class Conditioning(str, Enum): CONFIG_VERSION = 3 -DEFAULT_CONFIG: dict = { - "conditioning": { - # Width of the fused conditioning vector produced by the encoder's - # fusion MLP, consumed by every downstream trunk. - "out_dim": 128, - # false: stage 1 and stage 2 each construct their own ConditionEncoder - # with identical config but independent weights. true: one instance, - # shared by reference (halves the conditioning parameter count, - # forces a common representation). - "share_stages": False, - "particle": { - # "physical": a small MLP over log(mass)/charge, computable for - # any PDG code — generalizes beyond the training menu. - # "embedding": a learned nn.Embedding over a dense training-vocab - # index — memorizes the training menu; the generalization- - # comparison baseline, and the only mode compatible with - # stage2_model.particle_type.target = "embedding". - # "onehot": a fixed, unlearned vector — top (emb_dim - 1) PDG - # codes by training-set count, plus one "other" bin. NOT a - # reparameterization of "embedding": the vocabulary cap is the - # real difference. - "type": "physical", - # Width of this axis's vector. Under "onehot" this also sets the - # class count. - "emb_dim": 16, - # Depth of the sub-MLP under "physical". Ignored under - # "embedding"/"onehot". - "n_layers": 1, - }, - "material": { - "type": "physical", - "emb_dim": 16, - "n_layers": 1, - }, - }, - "stage1_model": { - # false skips building/training stage 1 entirely. The resulting - # checkpoint holds only stage 2 and cannot be rolled out. - "active": True, - # "flow": conditional flow matching (~10 ODE steps at inference). - # "ddpm": cosine-schedule diffusion baseline. - # "wgan": WGAN-GP, single forward pass at inference. - "generator": "flow", - # Trunk width — also the width of every expert under a routed trunk. - "hidden_dim": 256, - # Number of ResBlocks in the trunk, and in every expert under a - # routed trunk. - "n_res_blocks": 6, - "dropout": 0.0, - # Weight of this stage's loss in the total when both stages are - # active and non-adversarial. A WGAN stage's adversarial loss drives - # its own optimizer, so `lambda` scales only its non-adversarial - # auxiliary terms. - "lambda": 1.0, - "flow": { - # Width of the SinusoidalEmbedding for the flow time variable. - "time_dim": 64, - }, - "ddpm": { - "time_dim": 64, - "n_steps": 1000, - }, - "wgan": { - "noise_dim": 64, - "n_critic": 5, - "gp_weight": 10.0, - # 0.0 means "inherit train.lr" — not None, since the TOML writer - # has no null literal to round-trip. - "critic_lr": 0.0, - # 0 means "inherit stage1_model.hidden_dim/n_res_blocks" — same - # round-trip-friendly sentinel as critic_lr above. - "critic_hidden_dim": 0, - "critic_n_res_blocks": 0, - }, - "router": { - "enabled": False, - "type": "energy", # selects the Router impl from ROUTER_REGISTRY - "n_experts": 4, - "temperature": 0.5, # energy/pdg-router kwarg - "learn_centers": True, # energy/pdg-router kwarg - # energy-router kwargs: mutually exclusive optional learnable - # gate-sharpness modes. learn_width generalizes the shared - # `temperature` to one learnable width per expert; - # learn_temperature instead makes the single shared `temperature` - # itself learnable. Both are bounded to [width_min_ratio, - # width_max_ratio] * temperature so gate sharpness can't run away - # to a collapse-inducing extreme during training. - "learn_width": False, - "learn_temperature": False, - "width_min_ratio": 0.1, - "width_max_ratio": 10.0, - # Importance-CV^2 load-balancing aux loss weight (Shazeer et al. - # 2017). - "lambda_balance": 0.0, - # Entropy-regularization weight penalizing uniform/collapsed - # gating — a secondary guard against all experts' widths - # co-inflating together, which lambda_balance alone can't see. - "lambda_entropy": 0.0, - # Opt-in straight-through Gumbel-softmax train-time combine - # weights: the training forward pass samples a hard one-hot - # combination (matching eval-time top-1 dispatch exactly) while - # the backward pass still flows smooth gradient to every expert. - "gumbel": False, - "gumbel_tau_start": 1.0, - "gumbel_tau_end": 0.1, - "emb_dim": 8, # process/pdg-router kwarg: own pdg(/mat) embedding width - "hidden_dim": 64, # process-router kwarg: its classifier's hidden width - "lambda_proc": 0.0, # process-router kwarg: supervised process-CE weight - # type = "composed" routes on multiple axes at once (e.g. energy x - # pdg), each with its own expert count/hyperparameters. Axes are - # NOT in these defaults — set them as flat axis{i}_{field} keys - # instead of "n_experts", e.g. axis0_type = "energy", - # axis0_n_experts = 4, axis1_type = "pdg", axis1_n_experts = 3, - # axis1_emb_dim = 8. See giant.model.network._parse_composed_axes. - }, - }, - "stage2_model": { - # false trains stage 1 alone. giant rollout must then refuse the - # checkpoint; giant predict still works. - "active": True, - # "one_shot": predict all k_max slots simultaneously with padded - # slots masked from the loss (v0.2 behaviour). - # "autoregressive": emit one secondary at a time in descending-energy - # order. - "decoder": "autoregressive", - # As stage1_model.generator, but under "autoregressive" this is the - # objective for each token. - "generator": "wgan", - "hidden_dim": 256, - "n_res_blocks": 6, - "dropout": 0.0, - "lambda": 1.0, - # Maximum secondary slots. Under "one_shot" this is the fixed output - # width; under "autoregressive" it is a safety cap on the generation - # loop. - "k_max": 15, - # Width of the projected stage-1 outcome fed into stage 2's - # conditioning. - "context_dim": 64, - # "truth": the ground-truth stage-1 target vector, detached — stage- - # level teacher forcing (v0.2 behaviour). "sampled": stage 1's own - # sampled output, closing the train/inference gap at the cost of a - # sampling pass per batch and a moving target early in training. - "stage1_context": "truth", - "n_sec": { - # "head": a classifier over {0..k_max} on the condition encoding - # alone (no diffusion noise), callable independently at - # inference. - # "stop_token": an EOS-style implicit stop — accepted by the - # schema but not implemented in v0.3.0 (see validate_config). - # "truth": take n_sec from ground truth — standalone stage-2 - # evaluation only, never for rollout. - "mode": "head", - "lambda": 0.1, # cross-entropy weight for the head - }, - "particle_type": { - # The three targets mirror the three conditioning.particle - # modes. "onehot": class logits over conditioning.particle.emb_dim - # classes. "physical": regressed (log mass, charge). "embedding": - # regressed against conditioning's own particle embedding table - # (requires conditioning.particle.type = "embedding"). - "target": "onehot", - "lambda": 1.0, - # How a predicted "other" class becomes a concrete PDG code at - # rollout. "sample": draw from the empirical within-bucket - # distribution recorded at map-build time. "modal": always the - # most common member. "drop": discard the secondary. Read only - # under target = "onehot". - "other_policy": "sample", - }, - "autoregressive": { - # Canonical generation order. Single-valued for now; the key - # exists so an alternative ordering is not a config break. - "order": "energy_desc", - # How token i+1 sees tokens <= i. "markov": previous token plus - # running scalars (remaining energy budget, slot index) — a - # fixed-width summary. "attention": causal self-attention over - # all emitted tokens. - "history": "markov", - # "always": condition on the ground-truth previous secondary - # throughout training. "scheduled": scheduled sampling — - # interpolate toward the model's own prediction. "never": - # free-running from the start. - "teacher_forcing": "always", - "tf_p_start": 1.0, - "tf_p_end": 1.0, - "attn_n_heads": 4, - "attn_n_layers": 2, - }, - "flow": { - "time_dim": 64, - }, - "ddpm": { - "time_dim": 64, - "n_steps": 1000, - }, - "wgan": { - "noise_dim": 64, - "n_critic": 5, - "gp_weight": 10.0, - "critic_lr": 0.0, - "critic_hidden_dim": 0, - "critic_n_res_blocks": 0, - # Straight-through Gumbel temperature for the particle-type - # one-hot (distinct from router.gumbel_tau_start/_end, which - # anneal expert-combination weights). Read only under - # particle_type.target = "onehot". - "gumbel_tau_start": 1.0, - "gumbel_tau_end": 0.1, - }, - "router": { - # true: stage 2 shares stage 1's Router module instance, so - # expert i in stage 1 and expert i in stage 2 gate on identical - # conditions by construction — every other key in this block is - # then ignored. false: an independent router. - "tie_to_stage1": False, - "enabled": False, - "type": "energy", - "n_experts": 4, - "temperature": 0.5, - "learn_centers": True, - "learn_width": False, - "learn_temperature": False, - "width_min_ratio": 0.1, - "width_max_ratio": 10.0, - "lambda_balance": 0.0, - "lambda_entropy": 0.0, - "lambda_proc": 0.0, - "gumbel": False, - "gumbel_tau_start": 1.0, - "gumbel_tau_end": 0.1, - "emb_dim": 8, - "hidden_dim": 64, - }, - }, - "train": { - "epochs": 100, - "batch_size": 4096, - "lr": 3e-4, - "weight_decay": 0.01, # AdamW default — exposed so it can be tuned - "ema_decay": 0.9999, # EMA of model weights for sampling; 0 disables - "warmup_epochs": 5, - "val_fraction": 0.1, - # per-epoch val loss (not the marginal/KL validate_every pass) is - # capped to this many batches; 0 = full val set every epoch - "max_val_batches": 200, - "num_workers": 4, - "seed": 0, - "validate_every": 10, - "validate_steps": 10, - # Weights & Biases per-epoch metric logging. Default true in v0.3.0 - # (was opt-in false): the v0.3.0 work is a sequence of architecture - # comparisons, and a run that wasn't logged isn't comparable. Set - # false for throwaway/debug runs. - "wandb": True, - "wandb_project": "giant", - # "" means "use the checkpoint out_dir name" — not None, since the - # TOML writer has no null literal to round-trip. - "wandb_run_name": "", - # Batch-granularity metrics (loss/grad_norm/lr) are logged every N - # optimizer steps, not every batch — a single epoch can be tens of - # thousands of steps. Per-epoch metrics (the metrics.csv row) always - # log in full. - "wandb_log_every": 50, - }, -} +# --- Config dataclasses ----------------------------------------------------- +# +# These are the single source of truth for every default below. DEFAULT_CONFIG +# (a plain dict, for merge_cli_overrides/save_config/TOML round-tripping) is +# *generated* from GiantConfig().to_dict() rather than hand-maintained, so it +# cannot drift from the fallback defaults that build_models/build_critics +# (giant/model/network.py) and StageSpec.from_config (giant/training/trainers.py) +# read off these same dataclasses — see issues.md Issue 1. +# +# Each dataclass is frozen and carries an explicit from_dict/to_dict pair +# (mirroring StageSpec's established style in trainers.py) rather than a +# generic reflection-based helper, so every default is fully type-checkable. +# `lambda` is a Python keyword, so dict key "lambda" is always exposed as the +# field `lambda_weight`. +# +# Two sub-blocks — router and n_sec — carry genuinely dynamic keys that don't +# fit a fixed schema: composed-router `axis{i}_{field}` flags (see +# giant.model.network._parse_composed_axes) and pipeline.py's runtime-seeded +# `centers_init`, plus n_sec's `legacy_owner` (injected only by +# _migrate_legacy_model_config for v0.2 checkpoints). Both dataclasses carry +# an `extra: dict` catch-all so these keys round-trip losslessly without +# becoming named fields that would leak into every new run's config.toml. + + +@dataclass(frozen=True) +class ConditioningAxisConfig: + """One conditioning axis: `conditioning.particle` or `conditioning.material`.""" + + # "physical": a small MLP over log(mass)/charge, computable for any PDG + # code — generalizes beyond the training menu. + # "embedding": a learned nn.Embedding over a dense training-vocab index — + # memorizes the training menu; the generalization-comparison baseline, + # and the only mode compatible with stage2_model.particle_type.target = + # "embedding". + # "onehot": a fixed, unlearned vector — top (emb_dim - 1) PDG codes by + # training-set count, plus one "other" bin. NOT a reparameterization of + # "embedding": the vocabulary cap is the real difference. + type: str = "physical" + # Width of this axis's vector. Under "onehot" this also sets the class + # count. + emb_dim: int = 16 + # Depth of the sub-MLP under "physical". Ignored under "embedding"/"onehot". + n_layers: int = 1 + + @classmethod + def from_dict(cls, d: dict | None) -> "ConditioningAxisConfig": + d = d or {} + return cls( + type=d.get("type", "physical"), + emb_dim=d.get("emb_dim", 16), + n_layers=d.get("n_layers", 1), + ) + + def to_dict(self) -> dict: + return {"type": self.type, "emb_dim": self.emb_dim, "n_layers": self.n_layers} + + +@dataclass(frozen=True) +class ConditioningConfig: + # Width of the fused conditioning vector produced by the encoder's fusion + # MLP, consumed by every downstream trunk. + out_dim: int = 128 + # false: stage 1 and stage 2 each construct their own ConditionEncoder + # with identical config but independent weights. true: one instance, + # shared by reference (halves the conditioning parameter count, forces a + # common representation). + share_stages: bool = False + particle: ConditioningAxisConfig = field(default_factory=ConditioningAxisConfig) + material: ConditioningAxisConfig = field(default_factory=ConditioningAxisConfig) + + @classmethod + def from_dict(cls, d: dict | None) -> "ConditioningConfig": + d = d or {} + return cls( + out_dim=d.get("out_dim", 128), + share_stages=d.get("share_stages", False), + particle=ConditioningAxisConfig.from_dict(d.get("particle")), + material=ConditioningAxisConfig.from_dict(d.get("material")), + ) + + def to_dict(self) -> dict: + return { + "out_dim": self.out_dim, + "share_stages": self.share_stages, + "particle": self.particle.to_dict(), + "material": self.material.to_dict(), + } + + +@dataclass(frozen=True) +class FlowConfig: + # Width of the SinusoidalEmbedding for the flow time variable. + time_dim: int = 64 + + @classmethod + def from_dict(cls, d: dict | None) -> "FlowConfig": + d = d or {} + return cls(time_dim=d.get("time_dim", 64)) + + def to_dict(self) -> dict: + return {"time_dim": self.time_dim} + + +@dataclass(frozen=True) +class DdpmConfig: + time_dim: int = 64 + n_steps: int = 1000 + + @classmethod + def from_dict(cls, d: dict | None) -> "DdpmConfig": + d = d or {} + return cls(time_dim=d.get("time_dim", 64), n_steps=d.get("n_steps", 1000)) + + def to_dict(self) -> dict: + return {"time_dim": self.time_dim, "n_steps": self.n_steps} + + +@dataclass(frozen=True) +class Stage1WganConfig: + noise_dim: int = 64 + n_critic: int = 5 + gp_weight: float = 10.0 + # 0.0 means "inherit train.lr" — not None, since the TOML writer has no + # null literal to round-trip. + critic_lr: float = 0.0 + # 0 means "inherit stage1_model.hidden_dim/n_res_blocks" — same + # round-trip-friendly sentinel as critic_lr above. + critic_hidden_dim: int = 0 + critic_n_res_blocks: int = 0 + + @classmethod + def from_dict(cls, d: dict | None) -> "Stage1WganConfig": + d = d or {} + return cls( + noise_dim=d.get("noise_dim", 64), + n_critic=d.get("n_critic", 5), + gp_weight=d.get("gp_weight", 10.0), + critic_lr=d.get("critic_lr", 0.0), + critic_hidden_dim=d.get("critic_hidden_dim", 0), + critic_n_res_blocks=d.get("critic_n_res_blocks", 0), + ) + + def to_dict(self) -> dict: + return { + "noise_dim": self.noise_dim, + "n_critic": self.n_critic, + "gp_weight": self.gp_weight, + "critic_lr": self.critic_lr, + "critic_hidden_dim": self.critic_hidden_dim, + "critic_n_res_blocks": self.critic_n_res_blocks, + } + + +@dataclass(frozen=True) +class Stage2WganConfig(Stage1WganConfig): + # Straight-through Gumbel temperature for the particle-type one-hot + # (distinct from router.gumbel_tau_start/_end, which anneal + # expert-combination weights). Read only under particle_type.target = + # "onehot". + gumbel_tau_start: float = 1.0 + gumbel_tau_end: float = 0.1 + + @classmethod + def from_dict(cls, d: dict | None) -> "Stage2WganConfig": + d = d or {} + return cls( + noise_dim=d.get("noise_dim", 64), + n_critic=d.get("n_critic", 5), + gp_weight=d.get("gp_weight", 10.0), + critic_lr=d.get("critic_lr", 0.0), + critic_hidden_dim=d.get("critic_hidden_dim", 0), + critic_n_res_blocks=d.get("critic_n_res_blocks", 0), + gumbel_tau_start=d.get("gumbel_tau_start", 1.0), + gumbel_tau_end=d.get("gumbel_tau_end", 0.1), + ) + + def to_dict(self) -> dict: + return { + **super().to_dict(), + "gumbel_tau_start": self.gumbel_tau_start, + "gumbel_tau_end": self.gumbel_tau_end, + } + + +# Fixed router fields shared by stage1_model.router and stage2_model.router. +# Composed-router axis{i}_{field} keys and pipeline.py's runtime-seeded +# centers_init are NOT in this set — they land in RouterConfig.extra instead +# (see giant.model.network._parse_composed_axes). +_ROUTER_KNOWN_KEYS = frozenset( + { + "enabled", + "type", + "n_experts", + "temperature", + "learn_centers", + "learn_width", + "learn_temperature", + "width_min_ratio", + "width_max_ratio", + "lambda_balance", + "lambda_entropy", + "gumbel", + "gumbel_tau_start", + "gumbel_tau_end", + "emb_dim", + "hidden_dim", + "lambda_proc", + } +) + + +@dataclass(frozen=True) +class RouterConfig: + """`stage1_model.router`'s fixed fields. `extra` holds any key not named + below — composed-router `axis{i}_{field}` flags and pipeline.py's + runtime-seeded `centers_init` — so from_dict()/to_dict() round-trip + losslessly without this dataclass needing to know about them.""" + + enabled: bool = False + type: str = "energy" # selects the Router impl from ROUTER_REGISTRY + n_experts: int = 4 + temperature: float = 0.5 # energy/pdg-router kwarg + learn_centers: bool = True # energy/pdg-router kwarg + # energy-router kwargs: mutually exclusive optional learnable + # gate-sharpness modes. learn_width generalizes the shared `temperature` + # to one learnable width per expert; learn_temperature instead makes the + # single shared `temperature` itself learnable. Both are bounded to + # [width_min_ratio, width_max_ratio] * temperature so gate sharpness + # can't run away to a collapse-inducing extreme during training. + learn_width: bool = False + learn_temperature: bool = False + width_min_ratio: float = 0.1 + width_max_ratio: float = 10.0 + # Importance-CV^2 load-balancing aux loss weight (Shazeer et al. 2017). + lambda_balance: float = 0.0 + # Entropy-regularization weight penalizing uniform/collapsed gating — a + # secondary guard against all experts' widths co-inflating together, + # which lambda_balance alone can't see. + lambda_entropy: float = 0.0 + # Opt-in straight-through Gumbel-softmax train-time combine weights: the + # training forward pass samples a hard one-hot combination (matching + # eval-time top-1 dispatch exactly) while the backward pass still flows + # smooth gradient to every expert. + gumbel: bool = False + gumbel_tau_start: float = 1.0 + gumbel_tau_end: float = 0.1 + emb_dim: int = 8 # process/pdg-router kwarg: own pdg(/mat) embedding width + hidden_dim: int = 64 # process-router kwarg: its classifier's hidden width + lambda_proc: float = 0.0 # process-router kwarg: supervised process-CE weight + # type = "composed" routes on multiple axes at once (e.g. energy x pdg), + # each with its own expert count/hyperparameters. Axes are NOT in these + # defaults — set them as flat axis{i}_{field} keys instead of + # "n_experts", e.g. axis0_type = "energy", axis0_n_experts = 4, + # axis1_type = "pdg", axis1_n_experts = 3, axis1_emb_dim = 8. See + # giant.model.network._parse_composed_axes. + extra: dict = field(default_factory=dict) + + @classmethod + def from_dict(cls, d: dict | None) -> "RouterConfig": + d = d or {} + return cls( + enabled=d.get("enabled", False), + type=d.get("type", "energy"), + n_experts=d.get("n_experts", 4), + temperature=d.get("temperature", 0.5), + learn_centers=d.get("learn_centers", True), + learn_width=d.get("learn_width", False), + learn_temperature=d.get("learn_temperature", False), + width_min_ratio=d.get("width_min_ratio", 0.1), + width_max_ratio=d.get("width_max_ratio", 10.0), + lambda_balance=d.get("lambda_balance", 0.0), + lambda_entropy=d.get("lambda_entropy", 0.0), + gumbel=d.get("gumbel", False), + gumbel_tau_start=d.get("gumbel_tau_start", 1.0), + gumbel_tau_end=d.get("gumbel_tau_end", 0.1), + emb_dim=d.get("emb_dim", 8), + hidden_dim=d.get("hidden_dim", 64), + lambda_proc=d.get("lambda_proc", 0.0), + extra={k: v for k, v in d.items() if k not in _ROUTER_KNOWN_KEYS}, + ) + + def to_dict(self) -> dict: + return { + "enabled": self.enabled, + "type": self.type, + "n_experts": self.n_experts, + "temperature": self.temperature, + "learn_centers": self.learn_centers, + "learn_width": self.learn_width, + "learn_temperature": self.learn_temperature, + "width_min_ratio": self.width_min_ratio, + "width_max_ratio": self.width_max_ratio, + "lambda_balance": self.lambda_balance, + "lambda_entropy": self.lambda_entropy, + "gumbel": self.gumbel, + "gumbel_tau_start": self.gumbel_tau_start, + "gumbel_tau_end": self.gumbel_tau_end, + "emb_dim": self.emb_dim, + "hidden_dim": self.hidden_dim, + "lambda_proc": self.lambda_proc, + **self.extra, + } + + +@dataclass(frozen=True) +class Stage2RouterConfig(RouterConfig): + # true: stage 2 shares stage 1's Router module instance, so expert i in + # stage 1 and expert i in stage 2 gate on identical conditions by + # construction — every other key in this block is then ignored. false: + # an independent router. + tie_to_stage1: bool = False + + @classmethod + def from_dict(cls, d: dict | None) -> "Stage2RouterConfig": + d = d or {} + known = _ROUTER_KNOWN_KEYS | {"tie_to_stage1"} + return cls( + tie_to_stage1=d.get("tie_to_stage1", False), + enabled=d.get("enabled", False), + type=d.get("type", "energy"), + n_experts=d.get("n_experts", 4), + temperature=d.get("temperature", 0.5), + learn_centers=d.get("learn_centers", True), + learn_width=d.get("learn_width", False), + learn_temperature=d.get("learn_temperature", False), + width_min_ratio=d.get("width_min_ratio", 0.1), + width_max_ratio=d.get("width_max_ratio", 10.0), + lambda_balance=d.get("lambda_balance", 0.0), + lambda_entropy=d.get("lambda_entropy", 0.0), + gumbel=d.get("gumbel", False), + gumbel_tau_start=d.get("gumbel_tau_start", 1.0), + gumbel_tau_end=d.get("gumbel_tau_end", 0.1), + emb_dim=d.get("emb_dim", 8), + hidden_dim=d.get("hidden_dim", 64), + lambda_proc=d.get("lambda_proc", 0.0), + extra={k: v for k, v in d.items() if k not in known}, + ) + + def to_dict(self) -> dict: + return {"tie_to_stage1": self.tie_to_stage1, **super().to_dict()} + + +@dataclass(frozen=True) +class NSecConfig: + # "head": a classifier over {0..k_max} on the condition encoding alone + # (no diffusion noise), callable independently at inference. + # "stop_token": an EOS-style implicit stop — accepted by the schema but + # not implemented in v0.3.0 (see validate_config). + # "truth": take n_sec from ground truth — standalone stage-2 evaluation + # only, never for rollout. + mode: str = "head" + lambda_weight: float = 0.1 # dict key "lambda" — cross-entropy weight for the head + # Holds "legacy_owner" when injected by _migrate_legacy_model_config + # (v0.2 checkpoints only) — not a user-facing config.toml key. + extra: dict = field(default_factory=dict) + + @property + def legacy_owner(self) -> str | None: + return self.extra.get("legacy_owner") + + @classmethod + def from_dict(cls, d: dict | None) -> "NSecConfig": + d = d or {} + known = {"mode", "lambda"} + return cls( + mode=d.get("mode", "head"), + lambda_weight=d.get("lambda", 0.1), + extra={k: v for k, v in d.items() if k not in known}, + ) + + def to_dict(self) -> dict: + return {"mode": self.mode, "lambda": self.lambda_weight, **self.extra} + + +@dataclass(frozen=True) +class ParticleTypeConfig: + # The three targets mirror the three conditioning.particle modes. + # "onehot": class logits over conditioning.particle.emb_dim classes. + # "physical": regressed (log mass, charge). "embedding": regressed + # against conditioning's own particle embedding table (requires + # conditioning.particle.type = "embedding"). + target: str = "onehot" + lambda_weight: float = 1.0 # dict key "lambda" + # How a predicted "other" class becomes a concrete PDG code at rollout. + # "sample": draw from the empirical within-bucket distribution recorded + # at map-build time. "modal": always the most common member. "drop": + # discard the secondary. Read only under target = "onehot". + other_policy: str = "sample" + + @classmethod + def from_dict(cls, d: dict | None) -> "ParticleTypeConfig": + d = d or {} + return cls( + target=d.get("target", "onehot"), + lambda_weight=d.get("lambda", 1.0), + other_policy=d.get("other_policy", "sample"), + ) + + def to_dict(self) -> dict: + return {"target": self.target, "lambda": self.lambda_weight, "other_policy": self.other_policy} + + +@dataclass(frozen=True) +class AutoregressiveConfig: + # Canonical generation order. Single-valued for now; the key exists so + # an alternative ordering is not a config break. + order: str = "energy_desc" + # How token i+1 sees tokens <= i. "markov": previous token plus running + # scalars (remaining energy budget, slot index) — a fixed-width summary. + # "attention": causal self-attention over all emitted tokens. + history: str = "markov" + # "always": condition on the ground-truth previous secondary throughout + # training. "scheduled": scheduled sampling — interpolate toward the + # model's own prediction. "never": free-running from the start. + teacher_forcing: str = "always" + tf_p_start: float = 1.0 + tf_p_end: float = 1.0 + attn_n_heads: int = 4 + attn_n_layers: int = 2 + + @classmethod + def from_dict(cls, d: dict | None) -> "AutoregressiveConfig": + d = d or {} + return cls( + order=d.get("order", "energy_desc"), + history=d.get("history", "markov"), + teacher_forcing=d.get("teacher_forcing", "always"), + tf_p_start=d.get("tf_p_start", 1.0), + tf_p_end=d.get("tf_p_end", 1.0), + attn_n_heads=d.get("attn_n_heads", 4), + attn_n_layers=d.get("attn_n_layers", 2), + ) + + def to_dict(self) -> dict: + return { + "order": self.order, + "history": self.history, + "teacher_forcing": self.teacher_forcing, + "tf_p_start": self.tf_p_start, + "tf_p_end": self.tf_p_end, + "attn_n_heads": self.attn_n_heads, + "attn_n_layers": self.attn_n_layers, + } + + +@dataclass(frozen=True) +class Stage1ModelConfig: + # false skips building/training stage 1 entirely. The resulting + # checkpoint holds only stage 2 and cannot be rolled out. + active: bool = True + # "flow": conditional flow matching (~10 ODE steps at inference). + # "ddpm": cosine-schedule diffusion baseline. + # "wgan": WGAN-GP, single forward pass at inference. + generator: str = "flow" + # Trunk width — also the width of every expert under a routed trunk. + hidden_dim: int = 256 + # Number of ResBlocks in the trunk, and in every expert under a routed + # trunk. + n_res_blocks: int = 6 + dropout: float = 0.0 + # Weight of this stage's loss in the total when both stages are active + # and non-adversarial. A WGAN stage's adversarial loss drives its own + # optimizer, so `lambda` scales only its non-adversarial auxiliary terms. + lambda_weight: float = 1.0 # dict key "lambda" + flow: FlowConfig = field(default_factory=FlowConfig) + ddpm: DdpmConfig = field(default_factory=DdpmConfig) + wgan: Stage1WganConfig = field(default_factory=Stage1WganConfig) + router: RouterConfig = field(default_factory=RouterConfig) + + @classmethod + def from_dict(cls, d: dict | None) -> "Stage1ModelConfig": + d = d or {} + return cls( + active=d.get("active", True), + generator=d.get("generator", "flow"), + hidden_dim=d.get("hidden_dim", 256), + n_res_blocks=d.get("n_res_blocks", 6), + dropout=d.get("dropout", 0.0), + lambda_weight=d.get("lambda", 1.0), + flow=FlowConfig.from_dict(d.get("flow")), + ddpm=DdpmConfig.from_dict(d.get("ddpm")), + wgan=Stage1WganConfig.from_dict(d.get("wgan")), + router=RouterConfig.from_dict(d.get("router")), + ) + + def to_dict(self) -> dict: + return { + "active": self.active, + "generator": self.generator, + "hidden_dim": self.hidden_dim, + "n_res_blocks": self.n_res_blocks, + "dropout": self.dropout, + "lambda": self.lambda_weight, + "flow": self.flow.to_dict(), + "ddpm": self.ddpm.to_dict(), + "wgan": self.wgan.to_dict(), + "router": self.router.to_dict(), + } + + +@dataclass(frozen=True) +class Stage2ModelConfig: + # false trains stage 1 alone. giant rollout must then refuse the + # checkpoint; giant predict still works. + active: bool = True + # "one_shot": predict all k_max slots simultaneously with padded slots + # masked from the loss (v0.2 behaviour). + # "autoregressive": emit one secondary at a time in descending-energy + # order. + decoder: str = "autoregressive" + # As stage1_model.generator, but under "autoregressive" this is the + # objective for each token. + generator: str = "wgan" + hidden_dim: int = 256 + n_res_blocks: int = 6 + dropout: float = 0.0 + lambda_weight: float = 1.0 # dict key "lambda" + # Maximum secondary slots. Under "one_shot" this is the fixed output + # width; under "autoregressive" it is a safety cap on the generation loop. + k_max: int = 15 + # Width of the projected stage-1 outcome fed into stage 2's conditioning. + context_dim: int = 64 + # "truth": the ground-truth stage-1 target vector, detached — stage-level + # teacher forcing (v0.2 behaviour). "sampled": stage 1's own sampled + # output, closing the train/inference gap at the cost of a sampling pass + # per batch and a moving target early in training. + stage1_context: str = "truth" + n_sec: NSecConfig = field(default_factory=NSecConfig) + particle_type: ParticleTypeConfig = field(default_factory=ParticleTypeConfig) + autoregressive: AutoregressiveConfig = field(default_factory=AutoregressiveConfig) + flow: FlowConfig = field(default_factory=FlowConfig) + ddpm: DdpmConfig = field(default_factory=DdpmConfig) + wgan: Stage2WganConfig = field(default_factory=Stage2WganConfig) + router: Stage2RouterConfig = field(default_factory=Stage2RouterConfig) + + @classmethod + def from_dict(cls, d: dict | None) -> "Stage2ModelConfig": + d = d or {} + return cls( + active=d.get("active", True), + decoder=d.get("decoder", "autoregressive"), + generator=d.get("generator", "wgan"), + hidden_dim=d.get("hidden_dim", 256), + n_res_blocks=d.get("n_res_blocks", 6), + dropout=d.get("dropout", 0.0), + lambda_weight=d.get("lambda", 1.0), + k_max=d.get("k_max", 15), + context_dim=d.get("context_dim", 64), + stage1_context=d.get("stage1_context", "truth"), + n_sec=NSecConfig.from_dict(d.get("n_sec")), + particle_type=ParticleTypeConfig.from_dict(d.get("particle_type")), + autoregressive=AutoregressiveConfig.from_dict(d.get("autoregressive")), + flow=FlowConfig.from_dict(d.get("flow")), + ddpm=DdpmConfig.from_dict(d.get("ddpm")), + wgan=Stage2WganConfig.from_dict(d.get("wgan")), + router=Stage2RouterConfig.from_dict(d.get("router")), + ) + + def to_dict(self) -> dict: + return { + "active": self.active, + "decoder": self.decoder, + "generator": self.generator, + "hidden_dim": self.hidden_dim, + "n_res_blocks": self.n_res_blocks, + "dropout": self.dropout, + "lambda": self.lambda_weight, + "k_max": self.k_max, + "context_dim": self.context_dim, + "stage1_context": self.stage1_context, + "n_sec": self.n_sec.to_dict(), + "particle_type": self.particle_type.to_dict(), + "autoregressive": self.autoregressive.to_dict(), + "flow": self.flow.to_dict(), + "ddpm": self.ddpm.to_dict(), + "wgan": self.wgan.to_dict(), + "router": self.router.to_dict(), + } + + +@dataclass(frozen=True) +class TrainConfig: + epochs: int = 100 + batch_size: int = 4096 + lr: float = 3e-4 + weight_decay: float = 0.01 # AdamW default — exposed so it can be tuned + ema_decay: float = 0.9999 # EMA of model weights for sampling; 0 disables + warmup_epochs: int = 5 + val_fraction: float = 0.1 + # per-epoch val loss (not the marginal/KL validate_every pass) is capped + # to this many batches; 0 = full val set every epoch + max_val_batches: int = 200 + num_workers: int = 4 + seed: int = 0 + validate_every: int = 10 + validate_steps: int = 10 + # Weights & Biases per-epoch metric logging. Default true in v0.3.0 (was + # opt-in false): the v0.3.0 work is a sequence of architecture + # comparisons, and a run that wasn't logged isn't comparable. Set false + # for throwaway/debug runs. + wandb: bool = True + wandb_project: str = "giant" + # "" means "use the checkpoint out_dir name" — not None, since the TOML + # writer has no null literal to round-trip. + wandb_run_name: str = "" + # Batch-granularity metrics (loss/grad_norm/lr) are logged every N + # optimizer steps, not every batch — a single epoch can be tens of + # thousands of steps. Per-epoch metrics (the metrics.csv row) always log + # in full. + wandb_log_every: int = 50 + + @classmethod + def from_dict(cls, d: dict | None) -> "TrainConfig": + d = d or {} + return cls( + epochs=d.get("epochs", 100), + batch_size=d.get("batch_size", 4096), + lr=d.get("lr", 3e-4), + weight_decay=d.get("weight_decay", 0.01), + ema_decay=d.get("ema_decay", 0.9999), + warmup_epochs=d.get("warmup_epochs", 5), + val_fraction=d.get("val_fraction", 0.1), + max_val_batches=d.get("max_val_batches", 200), + num_workers=d.get("num_workers", 4), + seed=d.get("seed", 0), + validate_every=d.get("validate_every", 10), + validate_steps=d.get("validate_steps", 10), + wandb=d.get("wandb", True), + wandb_project=d.get("wandb_project", "giant"), + wandb_run_name=d.get("wandb_run_name", ""), + wandb_log_every=d.get("wandb_log_every", 50), + ) + + def to_dict(self) -> dict: + return { + "epochs": self.epochs, + "batch_size": self.batch_size, + "lr": self.lr, + "weight_decay": self.weight_decay, + "ema_decay": self.ema_decay, + "warmup_epochs": self.warmup_epochs, + "val_fraction": self.val_fraction, + "max_val_batches": self.max_val_batches, + "num_workers": self.num_workers, + "seed": self.seed, + "validate_every": self.validate_every, + "validate_steps": self.validate_steps, + "wandb": self.wandb, + "wandb_project": self.wandb_project, + "wandb_run_name": self.wandb_run_name, + "wandb_log_every": self.wandb_log_every, + } + + +@dataclass(frozen=True) +class GiantConfig: + """Root config dataclass — the single source of truth for every default + in DEFAULT_CONFIG below, which is generated from `GiantConfig().to_dict()` + rather than hand-maintained (see issues.md Issue 1).""" + + conditioning: ConditioningConfig = field(default_factory=ConditioningConfig) + stage1_model: Stage1ModelConfig = field(default_factory=Stage1ModelConfig) + stage2_model: Stage2ModelConfig = field(default_factory=Stage2ModelConfig) + train: TrainConfig = field(default_factory=TrainConfig) + + @classmethod + def from_dict(cls, d: dict | None) -> "GiantConfig": + d = d or {} + return cls( + conditioning=ConditioningConfig.from_dict(d.get("conditioning")), + stage1_model=Stage1ModelConfig.from_dict(d.get("stage1_model")), + stage2_model=Stage2ModelConfig.from_dict(d.get("stage2_model")), + train=TrainConfig.from_dict(d.get("train")), + ) + + def to_dict(self) -> dict: + return { + "conditioning": self.conditioning.to_dict(), + "stage1_model": self.stage1_model.to_dict(), + "stage2_model": self.stage2_model.to_dict(), + "train": self.train.to_dict(), + } + + +DEFAULT_CONFIG: dict = GiantConfig().to_dict() def git_hash() -> str: diff --git a/giant/model/network.py b/giant/model/network.py index bed790c..e946e20 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -7,6 +7,7 @@ import torch import torch.nn as nn import torch.nn.functional as F +from giant.config import ConditioningConfig, Stage1ModelConfig, Stage2ModelConfig from giant.constants import ( COND_DIM, COND_DIM_BASE, @@ -1593,78 +1594,84 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: particle_cfg = conditioning["particle"] material_cfg = conditioning["material"] particle_conditioning = particle_cfg["type"] - s1cfg = cfg["stage1_model"] - s2cfg = cfg["stage2_model"] - cond_out_dim = conditioning.get("out_dim", 128) + conditioning_cfg = ConditioningConfig.from_dict(conditioning) + s1_spec = Stage1ModelConfig.from_dict(cfg["stage1_model"]) + s2_spec = Stage2ModelConfig.from_dict(cfg["stage2_model"]) + cond_out_dim = conditioning_cfg.out_dim shared_cond_enc: ConditionEncoder | None = None - if conditioning.get("share_stages"): + if conditioning_cfg.share_stages: shared_cond_enc = ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim) result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} stage1_router: Router | None = None - if s1cfg.get("active", True): - router_cfg = s1cfg.get("router") or {} - if router_cfg.get("enabled"): + if s1_spec.active: + router_cfg = cfg["stage1_model"].get("router") or {} + if s1_spec.router.enabled: stage1_router = _build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, particle_conditioning) - generator = s1cfg.get("generator", "flow") - gen_sub = s1cfg.get(generator, {}) or {} - legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") - n_sec_head_k_max = s2cfg.get("k_max", K_MAX) if legacy_owner == "stage1" else None + generator = s1_spec.generator + # wgan has no time_dim concept (no diffusion/flow time variable) — + # matches the pre-dataclass .get("time_dim", 64) fallback, which + # always hit its default for a wgan sub-block too. + time_dim = getattr(s1_spec, generator).time_dim if generator != "wgan" else 64 + legacy_owner = s2_spec.n_sec.legacy_owner + n_sec_head_k_max = s2_spec.k_max if legacy_owner == "stage1" else None result["stage1"] = Stage1Model( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, particle_cfg=particle_cfg, material_cfg=material_cfg, - hidden_dim=s1cfg.get("hidden_dim", 256), - n_res_blocks=s1cfg.get("n_res_blocks", 6), + hidden_dim=s1_spec.hidden_dim, + n_res_blocks=s1_spec.n_res_blocks, cond_out_dim=cond_out_dim, - dropout=s1cfg.get("dropout", 0.0), + dropout=s1_spec.dropout, generator=generator, - time_dim=gen_sub.get("time_dim", 64), - noise_dim=(s1cfg.get("wgan") or {}).get("noise_dim", 64), + time_dim=time_dim, + noise_dim=s1_spec.wgan.noise_dim, router=stage1_router, n_sec_head_k_max=n_sec_head_k_max, cond_enc=shared_cond_enc, ) - if s2cfg.get("active", True): - decoder = s2cfg.get("decoder", "one_shot") - router_cfg = s2cfg.get("router") or {} + if s2_spec.active: + decoder = s2_spec.decoder + router_cfg = cfg["stage2_model"].get("router") or {} stage2_router: Router | None = None - if router_cfg.get("enabled"): - if router_cfg.get("tie_to_stage1") and stage1_router is not None: + if s2_spec.router.enabled: + if s2_spec.router.tie_to_stage1 and stage1_router is not None: stage2_router = stage1_router else: stage2_router = _build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab, particle_conditioning) - generator = s2cfg.get("generator", "wgan") - gen_sub = s2cfg.get(generator, {}) or {} - legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") - k_max = s2cfg.get("k_max", K_MAX) - particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} + generator = s2_spec.generator + # wgan has no time_dim concept — see the matching comment in stage 1 + # above. + time_dim = getattr(s2_spec, generator).time_dim if generator != "wgan" else 64 + legacy_owner = s2_spec.n_sec.legacy_owner + k_max = s2_spec.k_max + particle_type_cfg = s2_spec.particle_type.to_dict() if decoder == "autoregressive": - ar_cfg = s2cfg.get("autoregressive") or {} + ar_cfg = s2_spec.autoregressive result["stage2"] = Stage2Autoregressive( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, particle_cfg=particle_cfg, material_cfg=material_cfg, - hidden_dim=s2cfg.get("hidden_dim", 256), - n_res_blocks=s2cfg.get("n_res_blocks", 6), + hidden_dim=s2_spec.hidden_dim, + n_res_blocks=s2_spec.n_res_blocks, cond_out_dim=cond_out_dim, - context_dim=s2cfg.get("context_dim", 64), - dropout=s2cfg.get("dropout", 0.0), + context_dim=s2_spec.context_dim, + dropout=s2_spec.dropout, generator=generator, - time_dim=gen_sub.get("time_dim", 64), - noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), + time_dim=time_dim, + noise_dim=s2_spec.wgan.noise_dim, k_max=k_max, router=stage2_router, build_n_sec_head=legacy_owner != "stage1", particle_type_cfg=particle_type_cfg, - history=ar_cfg.get("history", "markov"), - attn_n_heads=ar_cfg.get("attn_n_heads", 4), - attn_n_layers=ar_cfg.get("attn_n_layers", 2), + history=ar_cfg.history, + attn_n_heads=ar_cfg.attn_n_heads, + attn_n_layers=ar_cfg.attn_n_layers, cond_enc=shared_cond_enc, ) else: @@ -1674,15 +1681,15 @@ def build_models(model_config: dict) -> dict[str, nn.Module | None]: mat_vocab=mat_vocab, particle_cfg=particle_cfg, material_cfg=material_cfg, - hidden_dim=s2cfg.get("hidden_dim", 256), - n_res_blocks=s2cfg.get("n_res_blocks", 6), + hidden_dim=s2_spec.hidden_dim, + n_res_blocks=s2_spec.n_res_blocks, cond_out_dim=cond_out_dim, - context_dim=s2cfg.get("context_dim", 64), + context_dim=s2_spec.context_dim, sec_dim=sec_dim, - dropout=s2cfg.get("dropout", 0.0), + dropout=s2_spec.dropout, generator=generator, - time_dim=gen_sub.get("time_dim", 64), - noise_dim=(s2cfg.get("wgan") or {}).get("noise_dim", 64), + time_dim=time_dim, + noise_dim=s2_spec.wgan.noise_dim, k_max=k_max, router=stage2_router, build_n_sec_head=legacy_owner != "stage1", @@ -1704,29 +1711,30 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: conditioning = cfg["conditioning"] particle_cfg = conditioning["particle"] material_cfg = conditioning["material"] - cond_out_dim = conditioning.get("out_dim", 128) - s1cfg = cfg["stage1_model"] - s2cfg = cfg["stage2_model"] + conditioning_cfg = ConditioningConfig.from_dict(conditioning) + cond_out_dim = conditioning_cfg.out_dim + s1_spec = Stage1ModelConfig.from_dict(cfg["stage1_model"]) + s2_spec = Stage2ModelConfig.from_dict(cfg["stage2_model"]) result: dict[str, nn.Module | None] = {"stage1": None, "stage2": None} - if s1cfg.get("active", True) and s1cfg.get("generator") == "wgan": + if s1_spec.active and s1_spec.generator == "wgan": result["stage1"] = CriticModel( pdg_vocab=pdg_vocab, mat_vocab=mat_vocab, particle_cfg=particle_cfg, material_cfg=material_cfg, in_dim=X_DIM, - hidden_dim=s1cfg.get("hidden_dim", 256), - n_res_blocks=s1cfg.get("n_res_blocks", 6), + hidden_dim=s1_spec.hidden_dim, + n_res_blocks=s1_spec.n_res_blocks, cond_out_dim=cond_out_dim, - dropout=s1cfg.get("dropout", 0.0), + dropout=s1_spec.dropout, stage="stage1", ) - if s2cfg.get("active", True) and s2cfg.get("generator") == "wgan": - k_max = s2cfg.get("k_max", K_MAX) - particle_type_cfg = s2cfg.get("particle_type") or {"target": "physical"} + if s2_spec.active and s2_spec.generator == "wgan": + k_max = s2_spec.k_max + particle_type_cfg = s2_spec.particle_type.to_dict() in_dim = stage2_trunk_sec_dim(particle_type_cfg, "wgan", k_max, particle_cfg["emb_dim"]) result["stage2"] = CriticModel( pdg_vocab=pdg_vocab, @@ -1734,12 +1742,12 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]: particle_cfg=particle_cfg, material_cfg=material_cfg, in_dim=in_dim, - hidden_dim=s2cfg.get("hidden_dim", 256), - n_res_blocks=s2cfg.get("n_res_blocks", 6), + hidden_dim=s2_spec.hidden_dim, + n_res_blocks=s2_spec.n_res_blocks, cond_out_dim=cond_out_dim, - dropout=s2cfg.get("dropout", 0.0), + dropout=s2_spec.dropout, stage="stage2", - context_dim=s2cfg.get("context_dim", 64), + context_dim=s2_spec.context_dim, ) return result diff --git a/giant/pipeline.py b/giant/pipeline.py index b6367ee..683053a 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -182,7 +182,7 @@ def run_setup_stage( # if both consumers are active. The material axis is independent. particle_cfg = cfg["conditioning"]["particle"] material_cfg = cfg["conditioning"]["material"] - particle_type_target = cfg["stage2_model"].get("particle_type", {}).get("target") + particle_type_target = config.ParticleTypeConfig.from_dict(cfg["stage2_model"].get("particle_type")).target pdg_topn_map: TopNMap | None = None if particle_cfg["type"] == "onehot" or particle_type_target == "onehot": @@ -379,7 +379,7 @@ def run_train_job( # The secondary type-index map depends on stage2_model.particle_type.target, # independently of conditioning's own onehot/embedding choice above # (physical stays untouched/None). - particle_type_target = cfg["stage2_model"].get("particle_type", {}).get("target", "physical") + particle_type_target = config.ParticleTypeConfig.from_dict(cfg["stage2_model"].get("particle_type")).target if particle_type_target == "onehot": assert setup.pdg_topn_map is not None sec_type_class_map = setup.pdg_topn_map.class_map diff --git a/giant/training/trainers.py b/giant/training/trainers.py index 8473a87..d6ffd6d 100644 --- a/giant/training/trainers.py +++ b/giant/training/trainers.py @@ -21,6 +21,7 @@ import torch import torch.nn.functional as F import torch.optim as optim +from giant.config import ParticleTypeConfig, Stage1ModelConfig, Stage2ModelConfig, TrainConfig from giant.constants import CONT_SLOT_DIM from giant.model.network import Router, stage2_type_dim from giant.model.schedule import ( @@ -94,7 +95,7 @@ class StageSpec: n_sec_lambda: float = 0.1 # particle-type target (stage 2 only) - particle_type: dict = field(default_factory=lambda: {"target": "physical"}) + particle_type: ParticleTypeConfig = field(default_factory=ParticleTypeConfig) particle_type_emb_dim: int = 16 # optimization @@ -128,48 +129,56 @@ class StageSpec: @classmethod def from_config(cls, cfg: dict, name: str, is_stage2: bool, steps_per_epoch: int) -> "StageSpec": - t = cfg["train"] - stage_cfg = cfg[f"{name}_model"] - router_cfg = stage_cfg.get("router") or {} - wgan_cfg = stage_cfg.get("wgan") or {} - ar_cfg = (stage_cfg.get("autoregressive") or {}) if is_stage2 else {} + t = TrainConfig.from_dict(cfg["train"]) + # n_sec/particle_type/decoder/autoregressive/wgan's gumbel_tau_* are + # stage-2-only concepts, always read off s2_spec (guarded by + # is_stage2 where the stage-1 StageSpec needs a different value) — + # historically n_sec/particle_type were read from stage2_model + # unconditionally even for the stage-1 StageSpec, preserved here for + # behavioral parity. stage_spec covers the fields both stage configs + # share structurally (generator, lambda, router, ddpm, and wgan's + # base fields — Stage2ModelConfig's sub-configs all subclass + # stage 1's). + s2_spec = Stage2ModelConfig.from_dict(cfg["stage2_model"]) + stage_spec = s2_spec if is_stage2 else Stage1ModelConfig.from_dict(cfg["stage1_model"]) return cls( name=name, is_stage2=is_stage2, - generator=stage_cfg["generator"], - decoder=stage_cfg.get("decoder", "one_shot") if is_stage2 else "one_shot", - lambda_weight=stage_cfg.get("lambda", 1.0), - n_sec_lambda=cfg["stage2_model"].get("n_sec", {}).get("lambda", 0.1), - particle_type=cfg["stage2_model"].get("particle_type") or {"target": "physical"}, + generator=stage_spec.generator, + decoder=s2_spec.decoder if is_stage2 else "one_shot", + lambda_weight=stage_spec.lambda_weight, + n_sec_lambda=s2_spec.n_sec.lambda_weight, + particle_type=s2_spec.particle_type, particle_type_emb_dim=cfg["conditioning"]["particle"]["emb_dim"], # train.* keys are all guaranteed by DEFAULT_CONFIG's deep-merge - # (giant/config.py), so they read directly; the field defaults - # below exist only for tests that construct StageSpec by hand. - lr=t["lr"], - weight_decay=t["weight_decay"], - ema_decay=t["ema_decay"], - warmup_epochs=t["warmup_epochs"], - epochs=t["epochs"], + # (giant/config.py), so TrainConfig.from_dict never has to fall + # back to a literal here; the field defaults below exist only + # for tests that construct StageSpec by hand. + lr=t.lr, + weight_decay=t.weight_decay, + ema_decay=t.ema_decay, + warmup_epochs=t.warmup_epochs, + epochs=t.epochs, steps_per_epoch=max(steps_per_epoch, 1), - lambda_balance=router_cfg.get("lambda_balance", 0.0), - lambda_proc=router_cfg.get("lambda_proc", 0.0), - lambda_entropy=router_cfg.get("lambda_entropy", 0.0), - gumbel_tau_start=router_cfg.get("gumbel_tau_start", 1.0), - gumbel_tau_end=router_cfg.get("gumbel_tau_end", 0.1), - teacher_forcing=ar_cfg.get("teacher_forcing", "always"), - tf_p_start=ar_cfg.get("tf_p_start", 1.0), - tf_p_end=ar_cfg.get("tf_p_end", 1.0), + lambda_balance=stage_spec.router.lambda_balance, + lambda_proc=stage_spec.router.lambda_proc, + lambda_entropy=stage_spec.router.lambda_entropy, + gumbel_tau_start=stage_spec.router.gumbel_tau_start, + gumbel_tau_end=stage_spec.router.gumbel_tau_end, + teacher_forcing=s2_spec.autoregressive.teacher_forcing if is_stage2 else cls.teacher_forcing, + tf_p_start=s2_spec.autoregressive.tf_p_start if is_stage2 else cls.tf_p_start, + tf_p_end=s2_spec.autoregressive.tf_p_end if is_stage2 else cls.tf_p_end, # AR self-sampling under scheduled/never teacher forcing reuses # train.validate_steps as its flow-matching ODE step count — no # dedicated config key for this (the autoregressive config lists # tf_p_start/tf_p_end/attn_n_heads/attn_n_layers only). - ar_sample_steps=t["validate_steps"], - ddpm_n_steps=stage_cfg.get("ddpm", {}).get("n_steps", 1000), - n_critic=wgan_cfg.get("n_critic", 5), - gp_weight=wgan_cfg.get("gp_weight", 10.0), - critic_lr=wgan_cfg.get("critic_lr", 0.0), - type_gumbel_tau_start=wgan_cfg.get("gumbel_tau_start", 1.0), - type_gumbel_tau_end=wgan_cfg.get("gumbel_tau_end", 0.1), + ar_sample_steps=t.validate_steps, + ddpm_n_steps=stage_spec.ddpm.n_steps, + n_critic=stage_spec.wgan.n_critic, + gp_weight=stage_spec.wgan.gp_weight, + critic_lr=stage_spec.wgan.critic_lr, + type_gumbel_tau_start=s2_spec.wgan.gumbel_tau_start if is_stage2 else cls.type_gumbel_tau_start, + type_gumbel_tau_end=s2_spec.wgan.gumbel_tau_end if is_stage2 else cls.type_gumbel_tau_end, ) @@ -227,7 +236,7 @@ class StageTrainer: self.router = _stage_router(self.model) self._modules = (self.model, *extra_modules) - self.particle_type_cfg = dict(spec.particle_type or {"target": "physical"}) + self.particle_type_cfg = spec.particle_type.to_dict() self.particle_type_emb_dim = spec.particle_type_emb_dim self.ema_decay = spec.ema_decay diff --git a/scripts/warm_setup_cache.py b/scripts/warm_setup_cache.py index 3168b9e..1feed1c 100644 --- a/scripts/warm_setup_cache.py +++ b/scripts/warm_setup_cache.py @@ -9,6 +9,7 @@ for the sidecar itself. from pathlib import Path +from giant import config as gconfig from giant.constants import K_MAX from giant.pipeline import run_setup_stage @@ -43,19 +44,25 @@ def run_warm_setup_cache( "type": router_type, "n_experts": n_experts, } - # A minimal v0.3 cfg — only the keys run_setup_stage actually reads - # (conditioning.{particle,material}.type, stage{1,2}_model.router). This - # CLI only ever configures one router (matching today's single + # Merged against DEFAULT_CONFIG (not a hand-rolled partial dict) so + # run_setup_stage always sees every key it might read (e.g. + # conditioning.particle.emb_dim, stage2_model.particle_type.target) at + # its real default, not silently missing/None — see issues.md Issue 1. + # This CLI only ever configures one router (matching today's single # --router-type flag), so it's placed on stage1_model; stage2_model's # stays disabled. - cfg = { - "conditioning": { - "particle": {"type": particle_conditioning}, - "material": {"type": material_conditioning}, + cfg = gconfig.merge_cli_overrides( + gconfig.DEFAULT_CONFIG, + None, + { + "conditioning": { + "particle": {"type": particle_conditioning}, + "material": {"type": material_conditioning}, + }, + "stage1_model": {"router": router_cfg}, + "stage2_model": {"router": {"enabled": False}, "k_max": K_MAX}, }, - "stage1_model": {"router": router_cfg}, - "stage2_model": {"router": {"enabled": False}, "k_max": K_MAX}, - } + ) run_setup_stage( Path(data), val_fraction=val_fraction, diff --git a/tests/test_config.py b/tests/test_config.py index 6aea480..cecb74b 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -27,6 +27,80 @@ def test_conditioning_enum_has_onehot(): } +# --------------------------------------------------------------------------- +# Config dataclasses (issues.md Issue 1) +# --------------------------------------------------------------------------- + + +def test_giant_config_to_dict_matches_default_config(): + """DEFAULT_CONFIG is generated from GiantConfig().to_dict() (not + hand-maintained), so the two cannot structurally drift apart — but this + pins the *equality* too, catching e.g. a stray in-place mutation of + DEFAULT_CONFIG added elsewhere after import.""" + assert gconfig.GiantConfig().to_dict() == gconfig.DEFAULT_CONFIG + + +@pytest.mark.parametrize( + "cls", + [ + gconfig.ConditioningAxisConfig, + gconfig.ConditioningConfig, + gconfig.FlowConfig, + gconfig.DdpmConfig, + gconfig.Stage1WganConfig, + gconfig.Stage2WganConfig, + gconfig.RouterConfig, + gconfig.Stage2RouterConfig, + gconfig.NSecConfig, + gconfig.ParticleTypeConfig, + gconfig.AutoregressiveConfig, + gconfig.Stage1ModelConfig, + gconfig.Stage2ModelConfig, + gconfig.TrainConfig, + gconfig.GiantConfig, + ], +) +def test_config_dataclass_from_dict_round_trips_through_to_dict(cls): + assert cls.from_dict(cls().to_dict()) == cls() + assert cls.from_dict(None) == cls() + + +def test_stage2_model_config_defaults_match_documented_v030_intent(): + """The two keys issues.md Issue 1 found drifted between DEFAULT_CONFIG + and build_models/StageSpec.from_config's own .get(key, default) + fallbacks — pinned directly against the dataclass that is now their + shared single source of truth.""" + spec = gconfig.Stage2ModelConfig() + assert spec.decoder == "autoregressive" + assert spec.particle_type.target == "onehot" + + +def test_router_config_extra_round_trips_composed_axis_keys(): + d = {"enabled": True, "type": "composed", "axis0_type": "energy", "axis0_n_experts": 4} + router = gconfig.RouterConfig.from_dict(d) + assert router.enabled is True + assert router.extra == {"axis0_type": "energy", "axis0_n_experts": 4} + assert router.to_dict()["axis0_type"] == "energy" + + +def test_stage2_router_config_tie_to_stage1_not_leaked_into_extra(): + router = gconfig.Stage2RouterConfig.from_dict({"tie_to_stage1": True}) + assert router.tie_to_stage1 is True + assert "tie_to_stage1" not in router.extra + + +def test_stage1_router_config_has_no_tie_to_stage1_key(): + """Stage 1's router schema must not gain stage 2's tie_to_stage1 key — + that would change every future run's saved config.toml shape.""" + assert "tie_to_stage1" not in gconfig.RouterConfig().to_dict() + + +def test_n_sec_config_extra_round_trips_legacy_owner(): + n_sec = gconfig.NSecConfig.from_dict({"mode": "head", "legacy_owner": "stage1"}) + assert n_sec.legacy_owner == "stage1" + assert n_sec.to_dict() == {"mode": "head", "lambda": 0.1, "legacy_owner": "stage1"} + + # --------------------------------------------------------------------------- # _deep_merge # --------------------------------------------------------------------------- diff --git a/tests/test_network.py b/tests/test_network.py index 5deab01..7857d39 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -12,6 +12,7 @@ from giant.model.network import ( Stage1Model, Stage2Autoregressive, Stage2OneShot, + build_critics, build_models, cat_col_layout, stage2_trunk_sec_dim, @@ -652,3 +653,58 @@ def test_build_models_share_stages_true_shared_params_are_in_both_stage_paramete assert shared_ids assert shared_ids <= {id(p) for p in stage1.parameters()} assert shared_ids <= {id(p) for p in stage2.parameters()} + + +# ── build_models/build_critics: DEFAULT_CONFIG fallback drift (issues.md #1) ─ + + +def _partial_model_config() -> dict: + """A hand-built model_config that omits stage2_model.decoder and + stage2_model.particle_type — deliberately not derived from + DEFAULT_CONFIG, unlike _minimal_model_config above. Regression fixture + for issues.md Issue 1: build_models/build_critics/StageSpec.from_config's + own fallback defaults for these two keys must equal DEFAULT_CONFIG's + ("autoregressive" / "onehot"), not the old, now-wrong v0.2-shaped + ("one_shot" / "physical") literals that used to live in three separate + .get(key, default) call sites.""" + return { + "pdg_vocab": 3, + "mat_vocab": 2, + "conditioning": { + "particle": {"type": "physical", "emb_dim": 4, "n_layers": 1}, + "material": {"type": "physical", "emb_dim": 4, "n_layers": 1}, + }, + "stage1_model": {"active": False}, + "stage2_model": { + "generator": "wgan", + "hidden_dim": 8, + "n_res_blocks": 1, + "k_max": 3, + # decoder and particle_type deliberately omitted + }, + } + + +def test_build_models_omitted_decoder_and_particle_type_match_default_config(): + built = build_models(_partial_model_config()) + assert isinstance(built["stage2"], Stage2Autoregressive) + assert built["stage2"].particle_type_cfg["target"] == "onehot" + + +def test_build_critics_omitted_particle_type_matches_default_config(): + cfg = _partial_model_config() + cfg["stage2_model"]["generator"] = "wgan" + onehot_critic = build_critics(cfg)["stage2"] + assert onehot_critic is not None + onehot_in_dim = onehot_critic.input_proj.in_features + + cfg["stage2_model"]["particle_type"] = {"target": "physical"} + physical_critic = build_critics(cfg)["stage2"] + assert physical_critic is not None + physical_in_dim = physical_critic.input_proj.in_features + + # onehot's per-slot type width is emb_dim classes vs. physical's fixed + # (log-mass, charge) pair — different unless emb_dim happens to be 2, so + # this also confirms the critic was actually built in onehot mode by + # default, not silently falling back to physical. + assert onehot_in_dim != physical_in_dim diff --git a/tests/test_train.py b/tests/test_train.py index 9a220fe..fc5781d 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -265,6 +265,12 @@ def _base_cfg(): "k_max": K_MAX, "context_dim": 16, "n_sec": {"mode": "head", "lambda": 0.1}, + # Explicit, not relying on the fallback default (which is + # "onehot", matching DEFAULT_CONFIG — see issues.md Issue 1): + # the "physical"-labelled cases below (and this fixture's own + # comment history) intend this as the base "physical" case, + # with "*_onehot"/"*_embedding" cases opting in explicitly. + "particle_type": {"target": "physical", "lambda": 1.0}, "flow": {"time_dim": 16}, "ddpm": {"time_dim": 16, "n_steps": 50}, "wgan": { @@ -566,6 +572,20 @@ def test_flow_stage_trainer_ddpm_not_implemented_for_stage2(): FlowDDPMStageTrainer(spec, torch.nn.Linear(1, 1), torch.device("cpu")) +def test_stage_spec_from_config_omitted_decoder_and_particle_type_match_default_config(): + """Regression for issues.md Issue 1: StageSpec.from_config's own fallback + defaults for stage2_model.decoder/particle_type must equal + DEFAULT_CONFIG's ("autoregressive" / "onehot"), not the old, now-wrong + ("one_shot" / "physical") literals a .get(key, default) call used to + supply when a hand-built cfg omitted these keys.""" + cfg = _base_cfg() + del cfg["stage2_model"]["decoder"] + del cfg["stage2_model"]["particle_type"] + spec = StageSpec.from_config(cfg, "stage2", is_stage2=True, steps_per_epoch=1) + assert spec.decoder == "autoregressive" + assert spec.particle_type.target == "onehot" + + # --- AR trainer wiring (v0.3.0 step 5) -------------------------------------- @@ -664,7 +684,7 @@ def test_wgan_onehot_one_shot_also_gets_grad_norm_instrumentation(): def test_wgan_physical_omits_grad_norm_slice_columns(): - cfg = _base_cfg() # default stage2_model has no particle_type -> "physical" + cfg = _base_cfg() # _base_cfg's stage2_model.particle_type.target is "physical" with tempfile.TemporaryDirectory() as tmp: out_dir = Path(tmp) / "run" _run_train(cfg, out_dir) -- 2.39.5 From 01acbfed6146d335963448fbbb9c2448f9ad28d5 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 14:47:29 +0200 Subject: [PATCH 26/30] Add unknown-key validation to config.toml merge (issues.md Issue 2) A typo like `n_res_block` for `n_res_blocks` previously merged cleanly, passed validate_config, and silently trained a model that didn't match config.toml's documented settings. merge_cli_overrides now rejects any key not present in DEFAULT_CONFIG's schema via validate_config_keys, with a did-you-mean suggestion, while still allowing the genuinely dynamic composed-router axis keys and centers_init. Checkpoint model_config loading is untouched, so old checkpoints keep loading. Co-Authored-By: Claude Sonnet 5 --- giant/config.py | 60 ++- issues.md | 1203 ++++++++++++++++++++++++++++++++++++++++++ tests/test_config.py | 91 ++++ 3 files changed, 1353 insertions(+), 1 deletion(-) create mode 100644 issues.md diff --git a/giant/config.py b/giant/config.py index 66d8cd7..59ec1e0 100644 --- a/giant/config.py +++ b/giant/config.py @@ -1,6 +1,8 @@ import copy +import difflib import hashlib import random +import re import subprocess import sys import tomllib @@ -1045,6 +1047,59 @@ def migrate_config(cfg: dict) -> dict: return new +# axis{i}_{field} composed-router keys (see network._parse_composed_axes) — field-name +# agnostic, matching network.py's own _AXIS_KEY_RE, since anything after axis{i}_ is +# passed straight through as a router kwarg there. +_AXIS_KEY_RE = re.compile(r"^axis\d+_.+$") + +# Paths (dotted, relative to the merged cfg root) that carry genuinely dynamic keys not +# in DEFAULT_CONFIG's fixed schema — composed-router axis{i}_{field} flags and +# pipeline.seed_router_centers's runtime-seeded centers_init (see RouterConfig.extra +# above). validate_config_keys allows any key under these paths through unconditionally +# apart from the axis-pattern/centers_init check below. +_DYNAMIC_ROUTER_PATHS = {"stage1_model.router", "stage2_model.router"} + + +def _unknown_key_error(full_path: str, key: str, valid_keys) -> ValueError: + hint = difflib.get_close_matches(key, list(valid_keys), n=1) + suggestion = f" — did you mean {hint[0]!r}?" if hint else "" + return ValueError(f"unknown config key {full_path!r}{suggestion}") + + +def _validate_keys(node: dict, default_node: dict, path: str) -> None: + for key, value in node.items(): + if path == "" and key == "meta": + continue + full_path = f"{path}.{key}" if path else key + if path in _DYNAMIC_ROUTER_PATHS and (key == "centers_init" or _AXIS_KEY_RE.match(key)): + continue + if key not in default_node: + raise _unknown_key_error(full_path, key, default_node.keys()) + if isinstance(value, dict) and isinstance(default_node[key], dict): + _validate_keys(value, default_node[key], full_path) + + +def validate_config_keys(cfg: dict) -> None: + """Reject any config key not part of the known v0.3 schema (DEFAULT_CONFIG's tree). + + Catches typos like `n_res_block` for `n_res_blocks` that would otherwise merge + cleanly, pass `validate_config`, and silently build the wrong model — see + issues.md Issue 2. + + Only exercised on the config.toml/CLI-overrides path (called from + `merge_cli_overrides` below). A checkpoint's `model_config` dict goes through + `network._migrate_legacy_model_config`/`build_models` instead and must keep + loading regardless of schema drift; old checkpoints predate this validator and + are never passed through here. + + Two areas are deliberately dynamic and excluded: `[meta]` (run provenance, no + DEFAULT_CONFIG counterpart), and `stage{1,2}_model.router`'s `axis{i}_{field}` + keys (composed-router axes, see `network._parse_composed_axes`) / + `centers_init` (runtime-seeded by `pipeline.seed_router_centers`). + """ + _validate_keys(cfg, DEFAULT_CONFIG, "") + + def merge_cli_overrides( defaults: dict, config_path: Path | None, @@ -1057,7 +1112,9 @@ def merge_cli_overrides( deep-merge (see `_deep_merge`) — the shape stage-prefixed CLI flags naturally produce. A v0.2-shaped TOML file is transparently migrated (`migrate_config`) before merging, so old configs on disk keep working - under the new schema. + under the new schema. The result is checked against the known schema + (`validate_config_keys`) before being returned, so a typo'd key raises + here rather than silently building the wrong model. """ cfg = copy.deepcopy(defaults) if config_path is not None: @@ -1075,6 +1132,7 @@ def merge_cli_overrides( cfg[section] = _deep_merge(cfg.get(section, {}), values) else: cfg[section] = values + validate_config_keys(cfg) return cfg diff --git a/issues.md b/issues.md new file mode 100644 index 0000000..abdcd5d --- /dev/null +++ b/issues.md @@ -0,0 +1,1203 @@ +# GIANT — architecture issues + +Software-engineering review of the `giant` codebase, conducted 2026-08-12 on branch +`v0.3.0-stage2-autoregressive` at commit `55332db`. + +This document is written to be read **without the context of the review conversation**. +Each issue states what the code does today, why that is a problem, how to verify the +claim independently, what the fix looks like, and — importantly — what the fix should +*not* touch. Line numbers are accurate as of `55332db`; if they have drifted, the +accompanying code excerpts and `grep` commands will still locate the code. + +## Baseline: what is already good + +Read this first, so the issues below are calibrated correctly. **This is a healthy +codebase.** The problems listed are about structural resilience as the v0.3.0 +architecture matrix grows, not about rot or breakage. + +- `pytest` — **725 tests pass in ~18 s**. Fast enough that there is no excuse for not + running it on every change. +- `uv run ruff check .`, `uv run ruff format --check .`, `uv run ty check .` — all clean, + and all three are enforced in CI (`.gitea/workflows/ci.yml`). +- Zero `TODO` / `FIXME` / `XXX` / `HACK` markers in `giant/` or `scripts/`. +- The internal import graph is **acyclic** with clean layering: + `constants → data → model → training → pipeline → cli`, and `analysis` almost fully + independent of the rest. +- Line coverage is **96–99.6 % on every core module** (`network.py` 98.6 %, + `trainers.py` 99.5 %, `rollout.py` 99.6 %, `transforms.py` 96.2 %, `pipeline.py` + 97.1 %, `analysis/catalog.py` 99.6 %). The single exception is `cli.py` at 35.8 % — + see Issue 4. +- Comment and docstring quality is unusually high. Docstrings routinely explain *why* + a decision was made, not just what the code does, and several call out their own + known limitations honestly. **Preserve this when refactoring.** A refactor that + deletes the reasoning in `giant/config.py`'s `DEFAULT_CONFIG` comments or + `giant/analysis/router_gating.py`'s module docstring is a net loss even if the code + gets shorter. +- `giant/analysis/` is the best-designed subsystem in the repo and should be treated as + the template the rest of the codebase moves toward. See Issue 11. + +## Issue index + +| # | Issue | Severity | Effort | Status | +|---|---|---|---|---| +| 1 | Config defaults are declared twice; `DEFAULT_CONFIG` and consumers already disagree | **High** | Medium | **Fixed** (`9bf5874`) | +| 2 | No unknown-key validation — a typo in `config.toml` silently trains the wrong model | **High** | Small | **Fixed** | +| 3 | `cli.py:train()` is a 58-parameter, 510-line fat controller | **High** | Medium | Open | +| 4 | `cli.py` is at 35.8 % coverage and holds untested override-precedence logic | **High** | Medium | Open | +| 5 | Inference bootstrap is duplicated verbatim between `predict` and `rollout` | **High** | Small | Open | +| 6 | Two independent v0.2→v0.3 migration surfaces encode the same knowledge | Medium | Medium | Open | +| 7 | Positional tuple contracts (9-tuple, 7-tuple) between data, model and training layers | Medium | Small | Open | +| 8 | `network.py` is 1745 lines holding three distinct modules | Medium | Small | Open | +| 9 | `scripts` is published as a top-level distribution package | Medium | Small | Open | +| 10 | `torch.load(weights_only=False)` — checkpoints are arbitrary pickles | Low | Medium | Open | +| 11 | Minor: `echo=print` threading, `particles → data.loader` layering | Low | Small | Open | + +--- + +## Issue 1 — Config defaults are declared twice, and the two declarations already disagree + +> **Status: Fixed, commit `9bf5874` on `v0.3.0-stage2-autoregressive`.** `giant/config.py` now +> declares a hierarchy of frozen dataclasses (`GiantConfig` and its nested blocks, following +> `StageSpec`'s existing style) as the single source of truth; `DEFAULT_CONFIG` is generated from +> `GiantConfig().to_dict()` rather than hand-maintained, and `build_models`/`build_critics` +> (`network.py`) and `StageSpec.from_config` (`trainers.py`) convert their dict input into these +> dataclasses at the top of each function and read attributes instead of duplicating +> `.get(key, literal)` defaults. Both drifted keys (`decoder`, `particle_type.target`) now resolve +> to `DEFAULT_CONFIG`'s documented v0.3.0 values (`"autoregressive"`/`"onehot"`) unconditionally. +> Router/`n_sec` sub-blocks keep a dict-shaped `extra` catch-all for their genuinely dynamic keys +> (composed-router `axis{i}_*`, runtime-seeded `centers_init`, legacy `legacy_owner`) rather than +> being fully typed — see the "Recommended fix" section below, which this diverges from slightly +> on that one point. Fixing the fallback surfaced two existing callers that had been silently +> depending on the old (wrong) default — a `tests/test_train.py` fixture and +> `scripts/warm_setup_cache.py`'s hand-built minimal config (now merged against `DEFAULT_CONFIG` +> instead of hand-rolled) — both fixed in the same commit. Regression tests added in +> `tests/test_config.py`, `tests/test_network.py`, `tests/test_train.py`, including one pinning +> `GiantConfig().to_dict() == DEFAULT_CONFIG` so this class of drift can't recur silently. +> Everything below this point describes the pre-fix state and is kept for historical context. + +**Severity: High. Effort: Medium. Risk if unfixed: silent wrong-model training.** + +**Location:** `giant/config.py:32` (`DEFAULT_CONFIG`) versus `giant/model/network.py:1570-1693` +(`build_models`), `giant/training/trainers.py:130-175` (`StageSpec.from_config`), +`giant/pipeline.py:381`. + +### What the code does today + +`DEFAULT_CONFIG` in `giant/config.py:32` is a fully-specified nested dict. Every key has +a value and most have an explanatory comment. It is, on paper, the single source of +truth for configuration defaults. + +The consumers do not treat it that way. They re-declare the same defaults inline via +`dict.get(key, default)`: + +```python +# giant/model/network.py, inside build_models +cond_out_dim = conditioning.get("out_dim", 128) +... +hidden_dim=s1cfg.get("hidden_dim", 256), +n_res_blocks=s1cfg.get("n_res_blocks", 6), +dropout=s1cfg.get("dropout", 0.0), +time_dim=gen_sub.get("time_dim", 64), +``` + +```python +# giant/training/trainers.py, inside StageSpec.from_config +lambda_weight=stage_cfg.get("lambda", 1.0), +n_sec_lambda=cfg["stage2_model"].get("n_sec", {}).get("lambda", 0.1), +ddpm_n_steps=stage_cfg.get("ddpm", {}).get("n_steps", 1000), +n_critic=wgan_cfg.get("n_critic", 5), +gp_weight=wgan_cfg.get("gp_weight", 10.0), +``` + +There are **132 `.get("key", default)` call sites across `giant/`**, concentrated in +`network.py` (55) and `trainers.py` (25). Each one is an independent copy of a value +that `DEFAULT_CONFIG` also declares. + +### Why it's a problem + +Because `cfg` is typed `dict` (i.e. `dict[str, Any]`), neither `ty` nor the tests can +see that the two declarations are supposed to agree. When they drift, nothing fails — +the model is simply built differently than the config file says. + +**This has already happened.** Two defaults currently disagree: + +| Key | `DEFAULT_CONFIG` says | `build_models` fallback says | +|---|---|---| +| `stage2_model.decoder` | `"autoregressive"` | `"one_shot"` (`network.py:1632`) | +| `stage2_model.particle_type.target` | `"onehot"` | `"physical"` (`network.py:1644`, `trainers.py:143`, `pipeline.py:381`) | + +Both are *architecturally load-bearing*: `decoder` selects between `Stage2Autoregressive` +and `Stage2OneShot` — two different networks — and `particle_type.target` selects between +categorical class logits and continuous `(log-mass, charge)` regression, which is the +exact axis the v0.3.0 redesign was created to change (see `CLAUDE.md`, "v0.3.0 — +Stage-2 autoregressive redesign"). + +### Is it currently a live bug? + +**No — it is currently latent.** Two things save it today: + +1. The training path always passes a fully-merged config. `giant/pipeline.py:446` builds + `model_config` from `cfg["stage1_model"]` / `cfg["stage2_model"]`, and `cfg` always + originates from `merge_cli_overrides(DEFAULT_CONFIG, ...)`, so every key is present + and no fallback fires. +2. The legacy path also populates the keys explicitly. + `_migrate_legacy_model_config` (`network.py:1446`) hard-codes + `"decoder": "one_shot"` and `"particle_type": {"target": "physical"}` into the dict it + returns, so a v0.2 checkpoint does not rely on the fallbacks either. +3. The tests build their configs by deep-copying `DEFAULT_CONFIG` (e.g. + `tests/test_network.py:611` `_minimal_model_config`), so they never exercise the + fallback branch. + +That is precisely what makes this dangerous rather than harmless. The fallbacks are +**unreachable by every current caller, untested, and wrong.** The first caller that +hand-builds a partial `model_config` — a new test, a notebook, a debugging script, a +future `giant train --stage2-only` path — silently gets the v0.2 architecture while the +config documentation promises the v0.3 one. + +### How to verify + +```bash +uv run python - <<'EOF' +import copy +from giant import config as gc +from giant.model.network import build_models + +full = copy.deepcopy(gc.DEFAULT_CONFIG) +print("DEFAULT_CONFIG stage2 decoder :", full["stage2_model"]["decoder"]) +print("DEFAULT_CONFIG particle_type.target:", full["stage2_model"]["particle_type"]["target"]) + +partial = { + "pdg_vocab": 3, "mat_vocab": 2, + "conditioning": full["conditioning"], + "stage1_model": {"hidden_dim": 8, "n_res_blocks": 1}, + "stage2_model": {"hidden_dim": 8, "n_res_blocks": 1, "k_max": 3}, +} +print("build_models fallback stage2 class :", type(build_models(partial)["stage2"]).__name__) +EOF +``` + +Observed output on `55332db`: + +``` +DEFAULT_CONFIG stage2 decoder : autoregressive +DEFAULT_CONFIG particle_type.target: onehot +build_models fallback stage2 class : Stage2OneShot +``` + +### Recommended fix + +Make the config **typed at the boundary**, so the defaults exist exactly once and the +type checker can see them. + +1. Define a frozen dataclass per config block in `giant/config.py`: + `ConditioningAxisConfig`, `ConditioningConfig`, `RouterConfig`, `WganConfig`, + `Stage1ModelConfig`, `Stage2ModelConfig`, `TrainConfig`, `GiantConfig`. The dataclass + field defaults become the *only* declaration of each default. Move the existing + `DEFAULT_CONFIG` comments onto the fields — they are the most valuable thing in that + file and must not be lost. +2. Give each a `from_dict(d)` classmethod that constructs from the merged TOML dict and + **rejects unknown keys** (that is Issue 2, and the same constructor solves both). +3. Change `build_models`, `build_critics` and `StageSpec.from_config` to take the + dataclasses instead of dicts. Every `.get(key, default)` becomes `cfg.key`. The 132 + duplicate defaults disappear by construction. +4. Keep `DEFAULT_CONFIG` as a derived artifact if anything still needs the dict shape + (`save_config`, `default_out_dir_name`, the setup-cache sidecar) — generate it from + the dataclasses via `dataclasses.asdict`, do not maintain it by hand. + +Precedent exists in this codebase and should be followed: +`@dataclass(frozen=True) StageSpec` (`trainers.py:79`) and `PlotSpec` +(`analysis/catalog.py:65`) already do exactly this, well. + +### Interim fix if the full change is too large + +If the dataclass migration is deferred, at minimum add a test that asserts the inline +fallbacks agree with `DEFAULT_CONFIG`, so the drift is caught: + +```python +def test_build_models_fallbacks_match_default_config(): + """Every `.get(key, default)` in build_models must use the same default + DEFAULT_CONFIG declares — see issues.md Issue 1.""" +``` + +and fix the two divergent values. Decide deliberately which way: if the fallbacks are +meant to encode *v0.2 legacy* behaviour, they are redundant (the legacy migration at +`network.py:1446` already sets both keys explicitly) and should be **deleted**, letting +a missing key raise `KeyError` instead of silently choosing an architecture. + +### Scope guard + +Do **not** change any actual default *value* as part of this refactor. The goal is to +make the two declarations agree and then have only one; changing what the model does is +a separate, physics-relevant decision that belongs in its own commit with its own +retraining. When resolving the two divergences above, the correct target is whatever +`DEFAULT_CONFIG` says (`autoregressive` / `onehot`), because that is the documented +v0.3.0 intent per `CLAUDE.md`. + +--- + +## Issue 2 — No unknown-key validation: a typo in `config.toml` silently trains the wrong model + +> **Status: Fixed.** `giant/config.py` now has `validate_config_keys(cfg)`, which walks a +> merged config dict recursively against `DEFAULT_CONFIG`'s tree (itself generated from +> the `GiantConfig` dataclasses added for Issue 1) and raises `ValueError` on any key not +> present there, with a `difflib`-based "did you mean" suggestion. `[meta]` is skipped +> unconditionally, and `stage{1,2}_model.router`'s `axis{i}_{field}` composed-router keys +> and runtime-seeded `centers_init` are allowed through explicitly. `merge_cli_overrides` +> calls it right before returning, so all three real entry points (`giant train`, +> `giant new-run`, `scripts/warm_setup_cache.py`) are covered automatically; checkpoint +> `model_config` loading (a separate code path, `network._migrate_legacy_model_config`) +> is untouched, so old checkpoints keep loading regardless of schema drift. This is the +> "standalone fallback" option from the recommended fix below rather than the +> dataclass-`from_dict`-rejects-unknown-keys option, specifically to keep the check scoped +> to the config.toml/CLI-overrides path without touching `validate_config`'s many direct +> unit-test callers or `run_train_job`'s redundant internal `validate_config` call. +> Regression tests added in `tests/test_config.py` cover top-level and nested typos, the +> axis/`centers_init` allowances, `[meta]` skipping, and both the file and CLI-overrides +> paths, plus the two real `configs/*.toml` fixtures that don't already fail +> `migrate_config` for unrelated reasons. Everything below this point describes the +> pre-fix state and is kept for historical context. + +**Severity: High. Effort: Small. Risk if unfixed: wasted GPU-days on a run that did not +use the setting you thought it did.** + +**Location:** `giant/config.py:456` (`_deep_merge`), `giant/config.py:628` +(`merge_cli_overrides`), `giant/config.py:661` (`validate_config`). + +### What the code does today + +`merge_cli_overrides` resolves configuration as `DEFAULT_CONFIG → TOML file → CLI +overrides`, deep-merging at each step. `_deep_merge` accepts any key: + +```python +def _deep_merge(base: dict, override: dict) -> dict: + result = dict(base) + for k, v in override.items(): + if isinstance(v, dict) and isinstance(result.get(k), dict): + result[k] = _deep_merge(result[k], v) + else: + result[k] = v # <-- unknown k is accepted and stored + return result +``` + +`migrate_config` explicitly preserves unrecognised sections +(`giant/config.py:612-615`: *"Anything else in the original dict (unrecognized top-level +sections) carries through untouched rather than being silently dropped"*). + +`validate_config` (`giant/config.py:661`) exists and is *good* — it catches cross-block +contradictions with genuinely excellent, actionable error messages (e.g. "`router.type = +'pdg'` builds its own training-vocab-scoped embedding, incompatible with +`conditioning.particle.type = 'physical'`"). But by design it only inspects keys it knows +about. It cannot detect a key that should not exist. + +### Why it's a problem + +A config file containing + +```toml +[stage1_model] +n_res_block = 12 # typo: should be n_res_blocks +``` + +merges cleanly, validates cleanly, trains to completion, and reports success — having +built a 6-block model. The stray `n_res_block` key is faithfully written back into the +run's saved `config.toml` by `save_config`, so the artifact of record also claims the +typo was a real setting. Nothing anywhere in the pipeline will ever say otherwise. + +The blast radius scales with the config surface. v0.3.0 introduced a large nested schema +(`conditioning` × `stage1_model` × `stage2_model` × per-generator sub-tables × router +sub-tables), which is a great deal of surface for typos, and the v0.3.0 workflow is +explicitly *"a sequence of architecture comparisons"* (`config.py`, the `wandb` default +comment). A comparison in which one arm silently ignored its distinguishing setting is +worse than no comparison — it produces a confident, wrong conclusion. + +There is a partial mitigation already: `warn_if_git_hash_mismatch` and +`warn_if_checkpoint_config_mismatch` warn about *provenance* drift. Neither looks at key +names. + +### How to verify + +```bash +uv run python - <<'EOF' +import tempfile, pathlib +from giant import config as gc + +p = pathlib.Path(tempfile.mkdtemp()) / "config.toml" +p.write_text("[meta]\nconfig_version = 3\n\n[stage1_model]\nn_res_block = 12\n") +cfg = gc.merge_cli_overrides(gc.DEFAULT_CONFIG, p, {}) +gc.validate_config(cfg) # passes +print("typo key survived :", cfg["stage1_model"]["n_res_block"]) +print("real key unchanged:", cfg["stage1_model"]["n_res_blocks"]) +EOF +``` + +### Recommended fix + +Add strict key validation against the known schema. Two options, in order of preference: + +1. **Preferred — fold into Issue 1.** If each config block becomes a dataclass with a + `from_dict` that raises on unknown keys, this issue is solved for free and cannot + regress. Raise a `ValueError` in the established house style, naming the offending + key, its section, and the closest valid key by edit distance + (`difflib.get_close_matches`) — that last part matters, because "unknown key + `n_res_block`; did you mean `n_res_blocks`?" is the message that actually saves the + run. + +2. **Standalone fallback.** Add `validate_config_keys(cfg)` to `giant/config.py` that + walks `cfg` alongside `DEFAULT_CONFIG` and raises on any key not present in the + defaults tree. Call it from `merge_cli_overrides` right before returning, so every + entry point gets it. + +Either way, the following need explicit allowances, since they legitimately carry keys +not in `DEFAULT_CONFIG`: + +- **`meta`** — holds `config_version`, `git_hash`, and run metadata from + `build_run_meta`, none of which are in the defaults tree. +- **Composed-router axis keys** — `stage{1,2}_model.router.axis{i}_{field}` are + deliberately dynamic and deliberately absent from `DEFAULT_CONFIG` (see the comment at + the end of the `router` block in `config.py`, and `_parse_composed_axes` in + `network.py:499`). Validate these against the `axis(\d+)_(\w+)` pattern plus the known + per-axis field names rather than against a fixed key list. +- **`stage2_model.n_sec.legacy_owner`** — injected by `_migrate_legacy_model_config`, not + a user-facing key. It appears in checkpoint `model_config` dicts, not in `config.toml`, + so it should not reach this validator; confirm that before assuming. + +### Scope guard + +Strict validation must apply to the **`config.toml` path only**, not to loading old +checkpoints. A v0.2 checkpoint's `model_config` is migrated by a different function +(`network._migrate_legacy_model_config`, see Issue 6) and must keep loading. Adding +strictness that rejects historical checkpoints would break `giant predict` / `giant +rollout` against every model trained so far. + +--- + +## Issue 3 — `cli.py:train()` is a 58-parameter, 510-line fat controller + +**Severity: High. Effort: Medium.** + +**Location:** `giant/cli.py:329-839`. + +### What the code does today + +Measured on `55332db`: + +| Command | Parameters | Lines | +|---|---|---| +| `train` | **58** | **510** | +| `new_run` | 31 | 188 | +| `predict` | 9 | 387 | +| `rollout` | 14 | 237 | + +Of `train`'s 510 lines, roughly 430 are the Typer signature (one `Annotated[...]` block +per flag) and roughly 80 are hand-written translation from flags into the nested +overrides dict. That translation follows the same shape five times over — for `train`, +`stage1_model`, `stage2_model`, `conditioning`, and the WGAN sub-tables: + +```python +cli_stage1_model: dict[str, object] = { + k: v for k, v in { + "hidden_dim": hidden_dim, + "n_res_blocks": n_blocks, + "dropout": dropout, + }.items() if v is not None +} +cli_stage1_model.update( + {k: v for k, v in { + "hidden_dim": stage1_hidden_dim, + "n_res_blocks": stage1_n_res_blocks, + "dropout": stage1_dropout, + }.items() if v is not None} +) +``` + +On top of that sits genuinely intricate precedence logic, correctly implemented but +expressed imperatively: + +- `--mode` sets `generator` on **both** stages; `--stage1-generator` / `--stage2-generator` + then override a single stage (`cli.py:713-720`). +- `--hidden-dim` / `--n-blocks` / `--dropout` are stage-1-only backward-compatible + shorthands; `--stage1-*` wins when both are given (`cli.py:676-700`). +- `--n-critic` / `--gp-weight` / `--noise-dim` / `--critic-lr` fan out to + `stage{1,2}_model.wgan.*`, with `--stage{1,2}-*` variants overriding per stage — built + by merging a shared dict under a stage-specific one (`cli.py:722-753`). +- `--emb-dim` and `--conditioning` each set **two** places + (`conditioning.particle.*` and `conditioning.material.*`) (`cli.py:702-710`). + +### Why it's a problem + +1. **The mapping is data, written as code.** Flag → dotted config path → fan-out rule is + a table. Written as 80 lines of copy-adapted dict comprehensions, correctness has to + be re-verified by reading, every time a flag is added. `giant/config.py:446` already + provides `_set_path(d, "stage1_model.wgan.n_critic", v)` — the primitive a table-driven + version needs. +2. **Adding a flag touches three places at once** (signature, the right comprehension, + the right precedence rule) with no mechanism forcing them to stay consistent. v0.3.0's + design explicitly anticipates more per-stage flags. +3. **It is the least-tested code in the repo** (Issue 4), because unit-testing a + 58-parameter Typer command means going through `CliRunner` with argv strings. +4. It obscures the ~15 lines that actually do something (`cli.py:825-839`): resolve the + out-dir, echo two lines, call `run_train_job`. + +### Recommended fix + +Extract the mapping into a pure, table-driven function in `giant/config.py` (not +`cli.py`), so it is importable and directly testable without Typer: + +```python +@dataclass(frozen=True) +class FlagSpec: + """One CLI flag's mapping into the config tree.""" + name: str # "stage1_hidden_dim" + paths: tuple[str, ...] # ("stage1_model.hidden_dim",) — >1 means fan-out + precedence: int = 0 # higher wins; per-stage flags outrank shorthands + +FLAG_SPECS: tuple[FlagSpec, ...] = ( + FlagSpec("hidden_dim", ("stage1_model.hidden_dim",), precedence=0), + FlagSpec("stage1_hidden_dim", ("stage1_model.hidden_dim",), precedence=1), + FlagSpec("mode", ("stage1_model.generator", "stage2_model.generator")), + FlagSpec("n_critic", ("stage1_model.wgan.n_critic", "stage2_model.wgan.n_critic")), + FlagSpec("emb_dim", ("conditioning.particle.emb_dim", "conditioning.material.emb_dim")), + ... +) + +def overrides_from_flags(values: dict[str, object]) -> dict: + """Build the nested overrides dict from {flag_name: value}, dropping + None (= flag not given) and applying precedence.""" +``` + +`train()` then becomes: parse `--batch-size auto`, collect `locals()`-style flag values, +call `overrides_from_flags`, `merge_cli_overrides`, `validate_config`, resolve out-dir, +call `run_train_job`. The 430-line Typer signature stays — that is irreducible, it *is* +the user interface — but it becomes the only bulk in the function. + +The precedence table also becomes self-documenting, which today requires reading three +separate inline comment blocks to reconstruct. + +### Scope guard + +- **Do not remove or rename any flag.** The backward-compatible shorthands + (`--hidden-dim`, `--n-blocks`, `--dropout`, `--mode`) exist because they predate the + per-stage flags and are in people's shell history and job scripts. Their surprising + stage-1-only scoping is documented behaviour, not a bug to fix. +- **Do not change precedence semantics.** Reproduce the current rules exactly, then + test them (Issue 4). Any intentional change is a separate commit. +- Preserve the explanatory comments at `cli.py:676-680` and `cli.py:713-719` — they + record *why* the precedence is the way it is. + +--- + +## Issue 4 — `cli.py` sits at 35.8 % coverage and holds untested override-precedence logic + +**Severity: High. Effort: Medium.** + +**Location:** `giant/cli.py` (1865 lines). + +### What the code does today + +Per-module line coverage from `coverage.xml`: + +| Module | Coverage | +|---|---| +| `rollout.py` | 99.6 % | +| `analysis/catalog.py` | 99.6 % | +| `training/trainers.py` | 99.5 % | +| `model/network.py` | 98.6 % | +| `pipeline.py` | 97.1 % | +| `data/transforms.py` | 96.2 % | +| `config.py` | 91.8 % | +| **`cli.py`** | **35.8 %** | + +Project total is 81.8 %. `cli.py` is not merely the lowest — it is a **60-point outlier** +against an otherwise uniformly high standard, and it is simultaneously the largest module +in the repo. + +Existing CLI tests (`tests/test_cli_train_overrides.py`, `tests/test_cli_new_run.py`, +`tests/test_cli_predict.py`) are the right idea and well-written; they simply cover a +fraction of the surface. + +### Why it's a problem + +The uncovered code is not boilerplate. It includes: + +- The entire flag → config override translation and its precedence rules (Issue 3). Every + "`--stage2-hidden-dim` beats `--hidden-dim`", "`--stage2-generator` beats `--mode`", + "`--n-critic` fans out to both stages unless `--stage1-n-critic` is given" rule is + currently **unverified by any test**. +- The complete inference bootstrap in `predict` and `rollout` — checkpoint loading, + normalizer reconstruction, conditioning-axis resolution, top-N map wiring (Issue 5). + This is the code where a divergence between the two commands produces silent + train/inference skew rather than a crash. +- The `--batch-size auto` parsing and estimation paths, and the six distinct + "retrain with the current code" checkpoint-compatibility guards. + +A failure here is expensive in a way a unit-test failure is not: a mis-scoped flag means +a multi-hour GPU run trains a model that differs from the one the experiment log claims. +Given `CLAUDE.md` describes v0.3.0 as a sequence of architecture comparisons, this is the +worst possible place for silent divergence. + +### How to verify + +```bash +uv run pytest -q --cov=giant --cov-report=term-missing:skip-covered 2>&1 | grep "cli.py" +``` + +### Recommended fix + +Coverage here is a *consequence* of Issue 3, not an independent goal. Sequence the work: + +1. **First, extract the testable logic** (Issue 3): `overrides_from_flags` in + `config.py`, and `load_for_inference` in a new `giant/checkpoint_io.py` (Issue 5). +2. **Then test the extracted functions directly** — no Typer, no `CliRunner`, no + filesystem. One test per precedence rule: + + ```python + def test_stage1_hidden_dim_beats_hidden_dim_shorthand(): ... + def test_mode_sets_both_stage_generators(): ... + def test_stage2_generator_overrides_mode_for_stage2_only(): ... + def test_n_critic_fans_out_to_both_wgan_subtables(): ... + def test_stage1_n_critic_overrides_shared_n_critic_for_stage1_only(): ... + def test_emb_dim_sets_both_conditioning_axes(): ... + ``` + +3. **Keep a thin layer of `CliRunner` smoke tests** over `train --help`, `predict`, + `rollout` to catch signature/wiring breakage, but do not try to reach high coverage + through the CLI surface — that is slow and brittle. + +Target: get `cli.py` to roughly the repo norm by *moving code out of it*, not by writing +elaborate CLI-invocation tests. A `cli.py` that is genuinely just argument declaration +plus delegation can sit at modest coverage without concern, because there will be +nothing in it left to get wrong. + +### Scope guard + +Do not add `# pragma: no cover` to close the gap. The gap is a real signal and the fix is +extraction. + +--- + +## Issue 5 — The inference bootstrap is duplicated verbatim between `predict` and `rollout` + +**Severity: High. Effort: Small. Risk if unfixed: silent train/inference skew.** + +**Location:** `giant/cli.py:1121-1201` (`predict`), `giant/cli.py:1534-1593` (`rollout`), +plus a third partial copy at `giant/analysis/router_gating.py:86-120`. + +### What the code does today + +`predict` and `rollout` each contain ~65 lines that are near-identical line-for-line: + +1. `ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False)` +2. Guard: no `model_config` → "retrain with the current code" → `Exit(1)` +3. Guard: no `sec_decoder` → same +4. Guard: no `normalizer.sec_phys` → same +5. `_conditioning_axes(model_cfg)` +6. `_load_pdg_topn_map(ckpt)` / `_load_mat_topn_map(ckpt)` +7. Guard: `particle_conditioning == "onehot"` but no `pdg_topn_map` → `Exit(1)` +8. Guard: `material_conditioning == "onehot"` but no `mat_topn_map` → `Exit(1)` +9. `pdg_map` / `mat_map` key-type coercion (`{int(k): v ...}` / `{str(k): v ...}`) +10. Three `Normalizer.from_dict(ckpt["normalizer"][...])` calls +11. `build_models(model_cfg)`, unpack `stage1` / `stage2` +12. Guard: either stage `None` → "needs both" → `Exit(1)` +13. `_load_model_weights(...)`, `.to(device).eval()` on both +14. `typer.echo(f"loaded checkpoint: ...")` +15. `gconfig.warn_if_checkpoint_config_mismatch(checkpoint)` + +Additionally, `_conditioning_axes` is **duplicated verbatim into a second module**. +`giant/analysis/router_gating.py:69` carries a byte-for-byte copy of +`giant/cli.py:74`, with the docstring openly acknowledging it: *"Mirrors +`giant.cli._conditioning_axes`."* + +```bash +grep -rn "def _conditioning_axes" giant/ +# giant/cli.py:74 +# giant/analysis/router_gating.py:69 +``` + +### Why it's a problem + +This is not cosmetic duplication. This code decides **how input features are assembled at +inference time** — which conditioning mode is used, which top-N vocabulary maps are +applied, which normalizer statistics are restored. If `predict` and `rollout` ever +diverge on any of those, the result is not an exception. It is two commands producing +subtly different physics from the same checkpoint, with no error and no warning. That +class of bug is found by noticing that a plot looks wrong, weeks later. + +The duplication is also **entirely untested** — it lives in the 35.8 %-covered region of +`cli.py` (Issue 4). And it is *already* growing: the third copy in `router_gating.py` +shows the pattern spreading into a package that is otherwise carefully isolated. + +The self-aware "Mirrors `giant.cli._conditioning_axes`" comment is the tell. When a +developer documents a copy rather than removing it, the missing abstraction has been +identified but not yet built. + +### Recommended fix + +Create `giant/checkpoint_io.py` (name it whatever fits; the point is that it is **not** +`cli.py`) exposing one function and one result object: + +```python +@dataclass(frozen=True) +class InferenceContext: + """Everything needed to run a trained checkpoint forward, resolved once.""" + stage1: nn.Module + stage2: nn.Module + cond_norm: Normalizer + tgt_norm: Normalizer + sec_phys_norm: Normalizer + pdg_map: dict[int, int] + mat_map: dict[str, int] + pdg_topn_map: TopNMap | None + mat_topn_map: TopNMap | None + particle_conditioning: str + material_conditioning: str + k_max: int + stage1_ddpm_steps: int + stage2_ddpm_steps: int + other_policy: str + model_config: dict + +def load_for_inference( + checkpoint: Path, + device: torch.device, + weights: str = "raw", + require_stage2: bool = True, +) -> InferenceContext: ... +``` + +Raise a dedicated `CheckpointCompatibilityError` (carrying the current, genuinely helpful +message text) instead of calling `typer.echo` + `typer.Exit`; the CLI catches it and does +the echo/exit. That keeps the module free of Typer and makes it unit-testable. + +`predict` and `rollout` each shrink by ~60 lines to a single call. `router_gating.py` +drops its `_conditioning_axes` copy and imports from the new module — note that +`router_gating` deliberately imports torch **lazily inside the function** +(`router_gating.py:88`) to keep the analysis workers' "polars/numpy only" contract; the +new module must be imported the same way there, and `checkpoint_io.py` must not be +imported at `analysis/` module scope. + +### Scope guard + +- Preserve every error message verbatim. They are specific and actionable + ("checkpoint's `conditioning.particle.type='onehot'` but has no `pdg_topn_map` — retrain + with the current code"), and users have seen them before. +- Preserve the `require_stage2` distinction: `giant predict` and `giant rollout` both need + both stages today, but `stage1_model.active = false` / `stage2_model.active = false` are + real config options (`DEFAULT_CONFIG`), so the parameter should exist rather than + hard-coding the requirement. +- Do not fold `giant/analysis/router_gating.py`'s router-specific loading + (`load_router`, which returns `None` for non-MoE checkpoints) into the shared function. + It has different semantics — absence is a normal outcome there, not an error. + +--- + +## Issue 6 — Two independent v0.2→v0.3 migration surfaces encode the same knowledge + +**Severity: Medium. Effort: Medium.** + +**Location:** `giant/config.py:514` (`migrate_config`) and +`giant/model/network.py:1446` (`_migrate_legacy_model_config`). + +### What the code does today + +v0.3.0 broke the config format: the v0.2 single `[train]` + `[model]` layout became +`[conditioning]` / `[stage1_model]` / `[stage2_model]` / `[train]`. That break has to be +absorbed in two different places, and today it is absorbed by two unrelated functions: + +- **`config.migrate_config`** translates a v0.2 **`config.toml`** on load. +- **`network._migrate_legacy_model_config`** translates a v0.2 **checkpoint's + `model_config` dict** on `build_models`. + +The split is acknowledged as deferred work in `migrate_config`'s own docstring: + +> *"Operates on the config.toml shape. A checkpoint's `model_config` dict (which +> additionally carries n_sec_head ownership and needs `network.build_models`'s +> cooperation) is a separate migration surface, deferred to the network.py refactor."* + +Both functions independently encode the same translation facts: + +| v0.2 concept | v0.3 destination | in `migrate_config` | in `_migrate_legacy_model_config` | +|---|---|---|---| +| `train.mode` / `model.mode` | `stage{1,2}_model.generator` | ✓ | ✓ | +| `model.emb_dim` | `conditioning.{particle,material}.emb_dim` | ✓ | ✓ | +| `model.conditioning` | `conditioning.{particle,material}.type` | ✓ | ✓ | +| `model.n_blocks` | `stage{1,2}_model.n_res_blocks` | ✓ | ✓ | +| `model.noise_dim` | `stage{1,2}_model.wgan.noise_dim` | ✓ | ✓ | +| conditioning MLP depth was always 2 | `conditioning.*.n_layers = 2` | ✓ | ✓ | +| `router.expert_hidden_dim` set → hard error | — | ✓ | ✓ (near-identical message) | +| n_sec head lived on stage 1 | `stage2_model.n_sec.legacy_owner` | ✗ | ✓ (only here) | + +The `expert_hidden_dim` rejection is the clearest symptom: the same policy, the same +reasoning, two hand-maintained copies of a ~10-line error message +(`config.py:581-591` and `network.py:1475-1489`). + +### Why it's a problem + +1. **Drift.** A future correction to the v0.2 interpretation must be applied to both. + Applying it to one produces a checkpoint that loads with different architecture than + its own config file describes. +2. **`legacy_owner` leaks into the builder.** Because the checkpoint migration is + downstream of the config migration rather than sharing it, `build_models` has to carry + legacy-specific branching at four sites (`network.py:1612`, `1642`, `1663`, `1688`): + + ```python + legacy_owner = (s2cfg.get("n_sec") or {}).get("legacy_owner") + n_sec_head_k_max = s2cfg.get("k_max", K_MAX) if legacy_owner == "stage1" else None + ... + build_n_sec_head=legacy_owner != "stage1", + ``` + + A builder for the current architecture should not need to know where v0.2 put its + `n_sec` head. +3. **The retention policy is undeclared.** `tests/legacy/network_v02_snapshot.py` is + **1009 lines** of frozen v0.2 network code kept purely so migration can be tested + against it. That is a substantial maintenance surface with no stated expiry. + +### Recommended fix + +1. **Extract the shared translation into one table.** A single + `_V02_TO_V03_TRANSLATION` mapping (old dotted key → tuple of new dotted keys) plus one + set of "v0.2 architectural facts with no config key" constants, consumed by both + functions. `config.py:475-511` already has the beginnings of this + (`_V02_TRAIN_PASSTHROUGH`, `_V02_MODEL_TO_BOTH_STAGES`, + `_V02_TRAIN_TO_BOTH_STAGES_WGAN`) — extend that pattern and share it. Put the shared + table in a neutral module (e.g. `giant/_migration.py`) so neither `config.py` nor + `network.py` has to import the other. +2. **Move `legacy_owner` handling out of `build_models`.** The migration function should + emit a `model_config` that `build_models` can consume without legacy branching — for + example by emitting an explicit `stage2_model.n_sec.owner` key that the *current* + schema also carries (with value `"stage2"` for new runs), so the builder reads one key + with two valid values rather than a nullable legacy sentinel. +3. **Write down the retention policy.** Add to `CLAUDE.md`: which v0.2 checkpoints must + remain loadable, until when, and what triggers dropping the shim and the 1009-line + snapshot. Without that, nobody will ever feel authorised to delete it. + +### Scope guard + +- **v0.2 checkpoints must keep loading** until the policy above says otherwise. There are + trained models on `/ceph` that predate v0.3.0 and analysis runs referencing them. +- Keep `tests/legacy/network_v02_snapshot.py` as a **frozen snapshot** — do not "clean it + up", reformat it, or make it share code with current `network.py`. Its entire value is + that it is an independent, unchanging record of what v0.2 did. If ruff/ty complain about + it, exclude it rather than edit it (it is already excluded from coverage via + `pyproject.toml`'s `omit = ["*/legacy/*"]`). + +--- + +## Issue 7 — Positional tuple contracts between the data, model and training layers + +**Severity: Medium. Effort: Small.** + +**Location:** `giant/data/transforms.py:863-890` (`build_features`), +`giant/data/dataset.py:229-237` (`StreamingStepsDataset` yield), +`giant/training/trainers.py:574-582` (unpack), `giant/cli.py:1233` (unpack). + +### What the code does today + +`build_features` returns a **bare 9-tuple**: + +```python +) -> tuple[ + np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, + np.ndarray, np.ndarray, Normalizer | None, Normalizer | None, +]: + """Assemble (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, + sec_type_idx) arrays. ...""" +``` + +The element *meanings* live only in the docstring. Call sites re-derive them positionally: + +```python +# giant/cli.py:1233 — predict +cond_cont, cond_cat, target_raw, _, _, _, _, _, _ = build_features(...) +``` + +`StreamingStepsDataset` yields a **7-tuple** of tensors (`dataset.py:229`), unpacked +positionally in the trainer: + +```python +# giant/training/trainers.py:574 +( + cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx, sec_type_idx, +) = _batch_to_device(batch, device) +``` + +and again, with a *different arity*, in the WGAN path: + +```python +# giant/training/trainers.py:737 +cond_cont, cond_cat, n_sec, sec_cont, sec_type_idx = batch_tensors +``` + +`_batch_to_device` is typed `(batch: tuple, device) -> tuple` — no element information at +all. + +### Why it's a problem + +1. **Invisible to the type checker.** `ty check` passes on all of it. Reordering two + `np.ndarray` elements — or inserting a new one in the middle — type-checks cleanly and + produces a model trained on scrambled features. Several of these arrays are + shape-compatible with each other (`n_sec`, `proc_idx` and `sec_type_idx` are all + integer arrays), so a swap may not even produce a shape error at runtime. +2. **This is the hottest contract in the codebase.** It crosses three layer boundaries + (`data` → `training`, `data` → `cli`) and is the mechanism by which the model receives + physics. A silent corruption here is the most expensive possible failure: it does not + crash, it just trains something wrong. +3. **The nine-underscore unpack** at `cli.py:1233` is unreadable and does not survive any + change to the tuple. +4. **Two different batch arities** for the same conceptual batch (7 in `_compute`, 5 in + `_stage2_real_and_fake`) means the reader must track which slice is in play. + +### Recommended fix + +Convert both to `NamedTuple`. Zero runtime cost, full `ty` visibility, tuple-unpacking +still works so migration is incremental: + +```python +class StepFeatures(NamedTuple): + """Output of build_features. Field order is load-bearing for existing + positional unpacking — append only, never insert or reorder.""" + cond_cont: np.ndarray + cond_cat: np.ndarray + target_s1: np.ndarray + n_sec: np.ndarray + sec_cont: np.ndarray + proc_idx: np.ndarray + sec_type_idx: np.ndarray + cond_normalizer: Normalizer | None + target_normalizer: Normalizer | None + +class StepBatch(NamedTuple): + cond_cont: torch.Tensor + cond_cat: torch.Tensor + target_s1: torch.Tensor + n_sec: torch.Tensor + sec_cont: torch.Tensor + proc_idx: torch.Tensor + sec_type_idx: torch.Tensor +``` + +Then `cli.py:1233` becomes `feats = build_features(...)` / `feats.cond_cont`, and +`_batch_to_device` gets the real signature `(batch: StepBatch, device) -> StepBatch`. + +Precedent exists in this codebase: `RolloutSummary` (`rollout.py:276`, a `TypedDict`) and +`SetupStageResult` (`pipeline.py:39`, a dataclass) already do this correctly. + +Note that `StreamingStepsDataset` passes batches through a PyTorch `DataLoader` with +`batch_size=None` and `num_workers > 0`, so the batch type must survive worker-process +pickling and `pin_memory`. `NamedTuple` does — it is a plain tuple subclass, and +`pin_memory` recurses into tuples — but **verify this with a real multi-worker run**, not +just the test suite, since the tests may run single-process. + +### Scope guard + +Do not reorder any existing fields while converting. The whole point of the change is to +make future reordering safe; performing one during the conversion, when nothing yet +protects against it, is the single riskiest version of this change. + +--- + +## Issue 8 — `network.py` is 1745 lines holding three distinct modules + +**Severity: Medium. Effort: Small (mechanical).** + +**Location:** `giant/model/network.py`. + +### What the code does today + +One file contains six unrelated concerns: + +| Lines (approx.) | Concern | +|---|---| +| 28-213 | Primitives: `SinusoidalEmbedding`, `cat_col_layout`, `_make_axis_mlp`, `ConditionEncoder`, `ContextAdapter`, `ResBlock` | +| 215-575 | **The entire router subsystem**: `Router` base, `register_router`/`build_router` registry, `EnergyRouter`, `PdgRouter`, `ProcessRouter`, `ComposedRouter`, `build_composed_router`, `_parse_composed_axes`, `_check_router_conditioning_compat`, `_build_router_from_cfg` | +| 577-727 | Trunks: `ExpertTrunk`, `_route_forward`, `Trunk`, `MonolithicTrunk`, `RoutedTrunk`, `build_trunk` | +| 729-881 | **History encoders**: `HistoryEncoder`, `MarkovHistory`, `_CausalAttnBlock`, `AttentionHistory` | +| 883-1443 | Top-level models: `Stage1Model`, `Stage2OneShot`, `Stage2Autoregressive`, `CriticModel` | +| 1446-1568 | **Legacy migration**: `_migrate_legacy_model_config`, `migrate_legacy_state_dict` | +| 1570-1745 | Builders: `build_models`, `build_critics` | + +The v0.3.0 refactor described in `CLAUDE.md` — *"`network.py` is refactored from ten +permutation classes into composable parts (encoder × trunk × objective)"* — clearly +landed and is a genuine improvement. The composition is visible and clean. The file +simply was not split to match. + +### Why it's a problem + +- The seams are already there in the class layout; the file just does not honour them. A + reader looking for `AttentionHistory` has no reason to expect it 800 lines into a file + whose name suggests "the network". +- The router subsystem alone is ~370 lines with its own registry, its own config parsing, + and its own compatibility validation. It is a subsystem, not a section. +- Legacy migration (Issue 6) sitting in the same file as the current builders is exactly + what lets `legacy_owner` bleed into `build_models`. +- It is a merge-conflict magnet on a repo with parallel feature branches + (`condor-gpu-train-rollout`, `v0.3.0-stage2-autoregressive`). + +### Recommended fix + +Split along the existing seams into `giant/model/`: + +``` +giant/model/ + layers.py # SinusoidalEmbedding, ResBlock, ContextAdapter, _make_axis_mlp + encoders.py # ConditionEncoder, cat_col_layout + routers.py # Router base + registry + all 4 router types + config parsing + trunks.py # Trunk, MonolithicTrunk, RoutedTrunk, ExpertTrunk, build_trunk + history.py # HistoryEncoder, MarkovHistory, AttentionHistory, _CausalAttnBlock + models.py # Stage1Model, Stage2OneShot, Stage2Autoregressive, CriticModel + builders.py # build_models, build_critics + _legacy.py # _migrate_legacy_model_config, migrate_legacy_state_dict + network.py # re-export shim: `from giant.model.layers import *` etc. +``` + +Keep `network.py` as a **re-export shim** so no import site outside `giant/model/` has to +change in the same commit. `network.py` is imported by `cli.py`, `sample.py`, +`trainers.py`, `pipeline.py`, `analysis/router_gating.py`, and heavily by the test suite — +`grep -rn "from giant.model.network import" giant/ tests/ | wc -l` before starting, to +size the blast radius if you do decide to update call sites (21 sites on `55332db`). + +This is a pure file-move refactor: no logic changes, and the 725-test suite plus `ty +check` is a strong safety net for it. + +### Scope guard + +Do it as its own commit, containing **only** moves and imports. Do not combine with Issue 1 +or Issue 6, both of which change behaviour in this file — a diff mixing moves with logic +changes is effectively unreviewable. + +--- + +## Issue 9 — `scripts` is published as a top-level distribution package + +**Severity: Medium. Effort: Small.** + +**Location:** `pyproject.toml`: + +```toml +[project.scripts] +giant = "giant.cli:app" +dwarf = "scripts.dwarf:app" + +[tool.hatch.build.targets.wheel] +packages = ["giant", "scripts"] +``` + +### What the code does today + +The repo's tooling CLI (`dwarf`) lives in a directory called `scripts/`, which is declared +as a wheel package and referenced by the console-script entry point as `scripts.dwarf:app`. +Installing `giant` therefore creates a top-level importable module named **`scripts`** in +`site-packages`. + +`scripts/` holds ten real modules: `dwarf.py`, `bump_dataset_version.py`, +`create_root_files.py`, `geometry_oracle.py`, `hparam_scan.py`, `migrate_geant_steps.py`, +`profile_analysis_costs.py`, `steps_to_parquet.py`, `steps_to_parquet_parallel.py`, +`warm_setup_cache.py`. + +### Why it's a problem + +`scripts` is one of the most generic names possible in the Python ecosystem. Consequences: + +1. **Collision.** Any other installed distribution that also ships a top-level `scripts` + package silently shadows or is shadowed by this one, depending on `sys.path` order. + The failure mode is an `ImportError` or — worse — importing someone else's `dwarf`-less + `scripts` and getting `AttributeError` at CLI startup. +2. **Environment-order fragility.** On the portal machines (`/work/lbogner`, shared with + other users, per `CLAUDE.md`), a stray `scripts/` directory in the CWD shadows the + installed package, because CWD precedes `site-packages` on `sys.path`. Running `dwarf` + from a directory that happens to contain a `scripts/` folder can break in a confusing + way. +3. **`scripts/` reads as "not part of the product"**, yet it is installed, has an entry + point, is covered by `[tool.coverage.run] source`, and has a full test suite + (`tests/test_dwarf.py` at 81.7 % coverage, plus `test_steps_to_parquet*.py`, + `test_create_root_files.py`, `test_bump_dataset_version.py`). Its name misrepresents + its status. + +### Recommended fix + +Move it under the `giant` namespace, where it cannot collide: + +``` +giant/tools/ # was scripts/ + dwarf.py + ... +``` + +```toml +[project.scripts] +giant = "giant.cli:app" +dwarf = "giant.tools.dwarf:app" + +[tool.hatch.build.targets.wheel] +packages = ["giant"] +``` + +Then update: + +- `[tool.coverage.run] source = ["giant"]` (drop the now-redundant `"scripts"`). +- All `from scripts.X import Y` in `tests/` (`grep -rn "scripts\." tests/`). +- Any `python scripts/foo.py` invocations in docs, `README.md`, `CLAUDE.md`, HTCondor + submit files, or shell history/job scripts on the portal machines. + +The `dwarf` **command name does not change**, so anything invoking `dwarf ...` keeps +working — only Python-level imports and direct `python scripts/...` paths move. + +### Scope guard + +Check `giant/analysis/condor.py` and any generated HTCondor submit descriptions for +hard-coded `scripts/` paths before moving. Jobs submitted to the cluster may reference the +path as it exists on `/work` or `/ceph`, and a rename that lands mid-flight breaks queued +jobs. Search for the literal string `scripts/` across the repo, not just Python imports. + +--- + +## Issue 10 — `torch.load(weights_only=False)`: checkpoints are arbitrary pickles + +**Severity: Low (given the threat model). Effort: Medium.** + +**Location:** `giant/cli.py:1122`, `giant/cli.py:1535`, +`giant/analysis/router_gating.py:93`, `giant/training/loop.py:153`. + +### What the code does today + +All four checkpoint loads pass `weights_only=False`: + +```python +ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) +``` + +This is *necessary* today, not careless: the checkpoint dict carries non-tensor objects +beyond raw state dicts — `model_config`, `pdg_map` / `mat_map`, `pdg_topn_map` / +`mat_topn_map`, and the `normalizer` sub-dict, assembled in +`giant/training/checkpoint.py:19` (`build_checkpoint`) from the `extras` argument. +`weights_only=True` would reject them. + +### Why it's worth recording + +`weights_only=False` means loading a checkpoint executes arbitrary pickle opcodes. The +practical threat model here is mild — checkpoints are produced by this codebase and live +on `/ceph/lbogner` — but the portal machines and `/ceph` are explicitly **shared with +other users** (`CLAUDE.md`, "Compute environment"), and `giant predict --checkpoint ` +will happily load a path someone else wrote. It is also the kind of thing that becomes a +blocker later, if a model is ever shared outside the group or published alongside a paper. + +Secondary practical cost: a pickled `model_config` cannot be inspected without importing +torch and unpickling. `dwarf status`-style tooling, or a human answering "what conditioning +mode was this trained with?", has to load the whole checkpoint. + +### Recommended fix + +Split the checkpoint into a tensor part and a JSON sidecar: + +- `ckpt.pt` — state dicts only, loadable with `weights_only=True`. +- `ckpt.meta.json` — `model_config`, vocab maps, top-N maps, normalizer statistics, + `epoch` / `global_step` / `best_val_loss`. + +This has real secondary benefits: the sidecar is greppable and diffable, so comparing two +runs' architectures becomes `diff` rather than a Python session, and it composes well +with the config-provenance machinery already in `config.py` +(`warn_if_checkpoint_config_mismatch`, `git_hash`). + +Note that `giant/data/setup_cache.py` already establishes the "JSON sidecar next to the +data" pattern for exactly this kind of non-tensor state — follow its conventions. + +### Scope guard + +This is a **format change**, so it needs a compatibility path: `load_for_inference` +(Issue 5) reads the sidecar if present and falls back to the pickled keys if not. Do not +attempt this before Issue 5 lands — with three separate copies of the loading code, a +format change means three separate compatibility paths. After Issue 5, it is one. + +Given the mild threat model, this is correctly the **lowest-priority** item here. It is +recorded so the decision is deliberate rather than accidental. + +--- + +## Issue 11 — Minor items + +### 11a — `echo=print` threaded through the pipeline instead of `logging` + +**Location:** `giant/pipeline.py:305` (`run_train_job(..., echo=print)`), +`giant/pipeline.py:86` (`run_setup_stage(..., echo=...)`); 52 `typer.echo` calls in +`cli.py`. + +Passing the output function as a parameter is *better* than hard-coding `typer.echo` deep +in the pipeline — it keeps `pipeline.py` free of Typer and makes output capturable in +tests. But at this scale it has costs: no severity levels (the `--num-workers` quota +warning at `pipeline.py:329` and routine progress output are indistinguishable), no +timestamps on long training runs, no way to route to a file without wrapping, and the +parameter has to be threaded through every function that might print. + +Suggested: standard `logging` with a Typer/rich handler configured once in `cli.py`. +Module-level `log = logging.getLogger(__name__)` replaces the threaded `echo`. Keep +`typer.echo` for genuine CLI output (the `device:` / `out_dir:` banners, error messages +before `Exit(1)`) — those are interface, not logs. + +Low priority; the current approach is defensible. Worth doing opportunistically if +`pipeline.py` is being touched anyway. + +### 11b — `giant.particles` imports from `giant.data.loader` + +**Location:** `giant/particles.py:30`. + +```python +if TYPE_CHECKING: + from giant.data.loader import TopNMap +``` + +`giant/particles.py` is a physics-domain module (PDG code decoding, mass/charge lookup). +`giant/data/loader.py` is data I/O. A domain module depending on an I/O module is a +layering inversion. + +Mitigating factors, which is why this is minor: the import is `TYPE_CHECKING`-guarded, so +there is **no runtime cycle**, and `ty check` passes. It is a design smell, not a defect. + +Suggested: move `TopNMap` to a neutral location — `giant/constants.py` or a new +`giant/types.py` — so both `particles.py` and `data/loader.py` depend on it rather than on +each other. Do this only if `TopNMap` is being touched for other reasons; it is not worth +a standalone commit. + +--- + +## Recommended sequence + +The issues are interdependent. This order minimises rework: + +1. **Issue 5** — extract `load_for_inference` into `giant/checkpoint_io.py`. Small, + self-contained, removes the highest-risk duplication, and unblocks Issue 10. +2. **Issue 3** — extract the flag→config mapping table into `config.py`. +3. **Issue 4** — test the two extracted units directly. Issues 3 and 5 make this cheap; + attempting it first means testing through `CliRunner`, which is slow and brittle. +4. **Issue 1 + Issue 2 together** — typed config dataclasses. One migration solves both, + and doing them separately means touching the same 132 call sites twice. +5. **Issue 6** — unify the migration surfaces (easier once config is typed). +6. **Issue 8** — split `network.py`. Pure file moves; do it as an isolated commit, after + the logic changes in Issues 1 and 6 have settled, to keep both diffs readable. +7. **Issue 7** — `NamedTuple` the feature/batch contracts. +8. **Issue 9** — move `scripts/` → `giant/tools/`. Independent of everything else; can be + done at any point. +9. **Issue 10** — checkpoint format split, if the threat model or a publication makes it + worthwhile. +10. **Issue 11** — opportunistically. + +## General guidance for whoever picks this up + +- **Run the full suite on every change.** `uv run pytest -q` takes 18 seconds. There is no + reason to batch up unverified changes. +- **Run all three checks before committing:** `uv run ruff check .`, + `uv run ruff format .`, `uv run ty check .`. CI enforces all three. +- **Do not change model behaviour while refactoring.** Several issues touch code that + determines what network gets built (Issues 1, 6) or what features it receives (Issues 5, + 7). A refactor that also changes a default silently invalidates every prior benchmark in + `/home/lars/knowledge-base/experiments/`. If a behaviour change is warranted, it is a + separate commit with a separate message saying so. +- **Preserve the comments.** This codebase's docstrings explain reasoning, trade-offs, and + known limitations to an unusually high standard. When code moves, the comments move with + it. When code is deleted, check whether the comment records a decision that still needs + recording elsewhere. +- **`giant/analysis/` is the template.** It is declarative where the rest of the codebase + is imperative (`PlotSpec` registry), explicit about its own contracts (the + `compute_partial` / `finalize` split, `chunkable=False`, the "polars/numpy only on + workers" boundary), and honest about its one exception (`router_gating.py`'s module + docstring explains precisely why it is allowed to import torch and why that is still + safe). When deciding how a refactored `config.py` or `cli.py` should look, look there. diff --git a/tests/test_config.py b/tests/test_config.py index cecb74b..ceb5e73 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -798,6 +798,97 @@ def test_validate_config_ar_checks_skipped_under_one_shot(): gconfig.validate_config(cfg) # must not raise +# --------------------------------------------------------------------------- +# validate_config_keys / merge_cli_overrides unknown-key rejection +# --------------------------------------------------------------------------- + + +def test_validate_config_keys_default_config_passes(): + gconfig.validate_config_keys(gconfig.DEFAULT_CONFIG) # must not raise + + +def test_validate_config_keys_rejects_unknown_top_level_key(): + cfg = _cfg_with(**{"bogus_section.foo": 1}) + try: + gconfig.validate_config_keys(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "bogus_section" in str(e) + + +def test_validate_config_keys_rejects_unknown_nested_key_with_close_match_hint(): + cfg = _cfg_with(**{"stage1_model.n_res_block": 12}) + try: + gconfig.validate_config_keys(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "stage1_model.n_res_block" in str(e) + assert "n_res_blocks" in str(e) + + +def test_validate_config_keys_allows_composed_router_axis_keys(): + cfg = _cfg_with( + **{ + "stage1_model.router.enabled": True, + "stage1_model.router.type": "composed", + "stage1_model.router.axis0_type": "energy", + "stage1_model.router.axis0_n_experts": 4, + "stage1_model.router.axis1_type": "pdg", + "stage1_model.router.axis1_emb_dim": 8, + } + ) + gconfig.validate_config_keys(cfg) # must not raise + + +def test_validate_config_keys_allows_centers_init(): + cfg = _cfg_with(**{"stage1_model.router.centers_init": [-1.0, 0.0, 1.0]}) + gconfig.validate_config_keys(cfg) # must not raise + + +def test_validate_config_keys_rejects_unrelated_unknown_router_key(): + cfg = _cfg_with(**{"stage1_model.router.n_expert": 4}) # typo for n_experts + try: + gconfig.validate_config_keys(cfg) + assert False, "expected ValueError" + except ValueError as e: + assert "stage1_model.router.n_expert" in str(e) + assert "n_experts" in str(e) + + +def test_validate_config_keys_skips_meta_section(): + cfg = _cfg_with() + cfg["meta"] = {"config_version": 3, "git_hash": "abc123"} + gconfig.validate_config_keys(cfg) # must not raise + + +def test_merge_cli_overrides_rejects_typo_in_toml_file(tmp_path): + path = tmp_path / "config.toml" + path.write_text("[meta]\nconfig_version = 3\n\n[stage1_model]\nn_res_block = 12\n") + try: + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}) + assert False, "expected ValueError" + except ValueError as e: + assert "n_res_block" in str(e) + + +def test_merge_cli_overrides_rejects_typo_in_cli_overrides(): + try: + gconfig.merge_cli_overrides( + gconfig.DEFAULT_CONFIG, + None, + {"stage1_model": {"n_res_block": 12}}, + ) + assert False, "expected ValueError" + except ValueError as e: + assert "n_res_block" in str(e) + + +@pytest.mark.parametrize("fixture_name", ["default.toml", "wgan_h128_b4_physical.toml"]) +def test_merge_cli_overrides_real_config_fixtures_pass_key_validation(fixture_name, monkeypatch): + monkeypatch.setattr(gconfig, "git_hash", lambda: "c3bf3abebfe29a10fe42b9cbafbb3460ab78d243") + gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, _CONFIGS_DIR / fixture_name, {}) # must not raise + + # --------------------------------------------------------------------------- # checkpoint config-mismatch warnings (unchanged surface, still exercised) # --------------------------------------------------------------------------- -- 2.39.5 From 2bfb1ab0564c2e3b8ba8de617ad99cfe9d72c02f Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 15:04:49 +0200 Subject: [PATCH 27/30] Extract giant train/new-run's CLI override mapping into a table-driven function (issues.md Issues 3 & 4) train()'s ~140-line hand-written flag->config translation (three different ad hoc "more specific flag wins" patterns) and new_run()'s near-verbatim copy are replaced by a shared FlagSpec/FLAG_SPECS table and overrides_from_flags() in config.py, reused by both commands. This makes the override/precedence logic directly unit-testable without CliRunner, closing coverage gaps that had zero tests (e.g. --emb-dim/--conditioning dual-axis fan-out, three of four WGAN knob legs, --stage2-generator overriding --mode, router's stage1-only asymmetry). No CLI flags, help text, or precedence semantics changed -- `giant train --help`/`giant new-run --help` are byte-identical before and after, and all previously-passing CliRunner tests still pass unmodified. Co-Authored-By: Claude Sonnet 5 --- giant/cli.py | 275 ++++++++---------------------- giant/config.py | 108 ++++++++++++ tests/test_cli_train_overrides.py | 80 +++++++++ tests/test_config.py | 119 +++++++++++++ 4 files changed, 381 insertions(+), 201 deletions(-) diff --git a/giant/cli.py b/giant/cli.py index ee49c1d..533034c 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -653,141 +653,57 @@ def train( ) raise typer.Exit(1) - cli_train = { - k: v - for k, v in { - "epochs": epochs, - "batch_size": batch_size_value, - "lr": lr, - "weight_decay": weight_decay, - "ema_decay": ema_decay, - "warmup_epochs": warmup_epochs, - "val_fraction": val_fraction, - "num_workers": num_workers, - "seed": seed, - "validate_every": validate_every, - "validate_steps": validate_steps, - "max_val_batches": max_val_batches, - "wandb": wandb, - "wandb_project": wandb_project, - "wandb_run_name": wandb_run_name, - "wandb_log_every": wandb_log_every, - }.items() - if v is not None - } - # --hidden-dim/--n-blocks/--dropout are stage-1-only shorthands kept for - # backward compatibility (they predate stage2_model having its own - # flags); --stage1-*/--stage2-* below are the explicit, discoverable - # per-stage flags, and take precedence when both are given. - cli_stage1_model: dict[str, object] = { - k: v - for k, v in { - "hidden_dim": hidden_dim, - "n_res_blocks": n_blocks, - "dropout": dropout, - }.items() - if v is not None - } - cli_stage1_model.update( - { - k: v - for k, v in { - "hidden_dim": stage1_hidden_dim, - "n_res_blocks": stage1_n_res_blocks, - "dropout": stage1_dropout, - }.items() - if v is not None - } - ) - cli_stage2_model: dict[str, object] = { - k: v - for k, v in { - "hidden_dim": stage2_hidden_dim, - "n_res_blocks": stage2_n_res_blocks, - "dropout": stage2_dropout, - "decoder": stage2_decoder.value if stage2_decoder is not None else None, - "k_max": stage2_k_max, - "context_dim": stage2_context_dim, - "stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, - }.items() - if v is not None - } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) - if cli_router: - cli_stage1_model["router"] = cli_router - - # --emb-dim/--conditioning set both conditioning axes (v0.2 had one - # shared value for particle+material). - cli_conditioning: dict[str, dict] = {} - if emb_dim is not None: - cli_conditioning["particle"] = {"emb_dim": emb_dim} - cli_conditioning["material"] = {"emb_dim": emb_dim} - if conditioning is not None: - cli_conditioning.setdefault("particle", {})["type"] = conditioning.value - cli_conditioning.setdefault("material", {})["type"] = conditioning.value - - overrides: dict[str, dict] = {} - if cli_train: - overrides["train"] = cli_train - if cli_stage1_model: - overrides["stage1_model"] = cli_stage1_model - if cli_stage2_model: - overrides["stage2_model"] = cli_stage2_model - if cli_conditioning: - overrides["conditioning"] = cli_conditioning - - # --mode/--n-critic/--gp-weight/--critic-lr/--noise-dim apply to BOTH - # stages by default (v0.2 had one global mode/wgan config shared by both - # — see giant.config.migrate_config's train.mode / - # train.{n_critic,gp_weight,critic_lr} precedent); the --stage1-*/ - # --stage2-* variants below override a single stage independently (decision - # 7), which is what actually enables e.g. `--stage1-generator flow - # --stage2-generator wgan`. - if mode is not None: - overrides.setdefault("stage1_model", {})["generator"] = mode.value - overrides.setdefault("stage2_model", {})["generator"] = mode.value - if stage1_generator is not None: - overrides.setdefault("stage1_model", {})["generator"] = stage1_generator.value - if stage2_generator is not None: - overrides.setdefault("stage2_model", {})["generator"] = stage2_generator.value - - shared_wgan_overrides = { - k: v - for k, v in { - "n_critic": n_critic, - "gp_weight": gp_weight, - "noise_dim": noise_dim, - "critic_lr": critic_lr, - }.items() - if v is not None + flag_values: dict[str, object] = { + "epochs": epochs, + "batch_size": batch_size_value, + "lr": lr, + "weight_decay": weight_decay, + "ema_decay": ema_decay, + "warmup_epochs": warmup_epochs, + "val_fraction": val_fraction, + "num_workers": num_workers, + "seed": seed, + "validate_every": validate_every, + "validate_steps": validate_steps, + "max_val_batches": max_val_batches, + "wandb": wandb, + "wandb_project": wandb_project, + "wandb_run_name": wandb_run_name, + "wandb_log_every": wandb_log_every, + "hidden_dim": hidden_dim, + "n_blocks": n_blocks, + "dropout": dropout, + "stage1_hidden_dim": stage1_hidden_dim, + "stage1_n_res_blocks": stage1_n_res_blocks, + "stage1_dropout": stage1_dropout, + "stage2_hidden_dim": stage2_hidden_dim, + "stage2_n_res_blocks": stage2_n_res_blocks, + "stage2_dropout": stage2_dropout, + "stage2_decoder": stage2_decoder.value if stage2_decoder is not None else None, + "stage2_k_max": stage2_k_max, + "stage2_context_dim": stage2_context_dim, + "stage2_stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, + "mode": mode.value if mode is not None else None, + "stage1_generator": stage1_generator.value if stage1_generator is not None else None, + "stage2_generator": stage2_generator.value if stage2_generator is not None else None, + "conditioning": conditioning.value if conditioning is not None else None, + "emb_dim": emb_dim, + "router_config": cli_router or None, + "n_critic": n_critic, + "gp_weight": gp_weight, + "noise_dim": noise_dim, + "critic_lr": critic_lr, + "stage1_n_critic": stage1_n_critic, + "stage1_gp_weight": stage1_gp_weight, + "stage1_noise_dim": stage1_noise_dim, + "stage1_critic_lr": stage1_critic_lr, + "stage2_n_critic": stage2_n_critic, + "stage2_gp_weight": stage2_gp_weight, + "stage2_noise_dim": stage2_noise_dim, + "stage2_critic_lr": stage2_critic_lr, } - stage1_wgan_overrides = { - k: v - for k, v in { - "n_critic": stage1_n_critic, - "gp_weight": stage1_gp_weight, - "noise_dim": stage1_noise_dim, - "critic_lr": stage1_critic_lr, - }.items() - if v is not None - } - stage2_wgan_overrides = { - k: v - for k, v in { - "n_critic": stage2_n_critic, - "gp_weight": stage2_gp_weight, - "noise_dim": stage2_noise_dim, - "critic_lr": stage2_critic_lr, - }.items() - if v is not None - } - for stage_name, stage_specific in ( - ("stage1_model", stage1_wgan_overrides), - ("stage2_model", stage2_wgan_overrides), - ): - stage_wgan = {**shared_wgan_overrides, **stage_specific} - if stage_wgan: - overrides.setdefault(stage_name, {}).setdefault("wgan", {}).update(stage_wgan) + overrides = gconfig.overrides_from_flags(flag_values) cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) gconfig.validate_config(cfg) @@ -912,75 +828,32 @@ def new_run( (with the full dataset-derived meta section), so this scaffold's meta section is just a placeholder recording what was asked for and when. """ - cli_train = { - k: v - for k, v in { - "epochs": epochs, - "batch_size": batch_size, - "lr": lr, - }.items() - if v is not None - } - cli_stage1_model: dict[str, object] = { - k: v - for k, v in { - "hidden_dim": hidden_dim, - "n_res_blocks": n_blocks, - "dropout": dropout, - }.items() - if v is not None - } - cli_stage1_model.update( - { - k: v - for k, v in { - "hidden_dim": stage1_hidden_dim, - "n_res_blocks": stage1_n_res_blocks, - "dropout": stage1_dropout, - }.items() - if v is not None - } - ) - cli_stage2_model: dict[str, object] = { - k: v - for k, v in { - "hidden_dim": stage2_hidden_dim, - "n_res_blocks": stage2_n_res_blocks, - "dropout": stage2_dropout, - "decoder": stage2_decoder.value if stage2_decoder is not None else None, - "k_max": stage2_k_max, - "context_dim": stage2_context_dim, - "stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, - }.items() - if v is not None - } cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis) - if cli_router: - cli_stage1_model["router"] = cli_router - cli_conditioning: dict[str, dict] = {} - if emb_dim is not None: - cli_conditioning["particle"] = {"emb_dim": emb_dim} - cli_conditioning["material"] = {"emb_dim": emb_dim} - if conditioning is not None: - cli_conditioning.setdefault("particle", {})["type"] = conditioning.value - cli_conditioning.setdefault("material", {})["type"] = conditioning.value - - overrides: dict[str, dict] = {} - if cli_train: - overrides["train"] = cli_train - if cli_stage1_model: - overrides["stage1_model"] = cli_stage1_model - if cli_stage2_model: - overrides["stage2_model"] = cli_stage2_model - if cli_conditioning: - overrides["conditioning"] = cli_conditioning - if mode is not None: - overrides.setdefault("stage1_model", {})["generator"] = mode.value - overrides.setdefault("stage2_model", {})["generator"] = mode.value - if stage1_generator is not None: - overrides.setdefault("stage1_model", {})["generator"] = stage1_generator.value - if stage2_generator is not None: - overrides.setdefault("stage2_model", {})["generator"] = stage2_generator.value + flag_values: dict[str, object] = { + "epochs": epochs, + "batch_size": batch_size, + "lr": lr, + "hidden_dim": hidden_dim, + "n_blocks": n_blocks, + "dropout": dropout, + "stage1_hidden_dim": stage1_hidden_dim, + "stage1_n_res_blocks": stage1_n_res_blocks, + "stage1_dropout": stage1_dropout, + "stage2_hidden_dim": stage2_hidden_dim, + "stage2_n_res_blocks": stage2_n_res_blocks, + "stage2_dropout": stage2_dropout, + "stage2_decoder": stage2_decoder.value if stage2_decoder is not None else None, + "stage2_k_max": stage2_k_max, + "stage2_context_dim": stage2_context_dim, + "stage2_stage1_context": stage2_stage1_context.value if stage2_stage1_context is not None else None, + "mode": mode.value if mode is not None else None, + "stage1_generator": stage1_generator.value if stage1_generator is not None else None, + "stage2_generator": stage2_generator.value if stage2_generator is not None else None, + "conditioning": conditioning.value if conditioning is not None else None, + "emb_dim": emb_dim, + "router_config": cli_router or None, + } + overrides = gconfig.overrides_from_flags(flag_values) cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config, overrides) gconfig.validate_config(cfg) diff --git a/giant/config.py b/giant/config.py index 59ec1e0..c7d10ec 100644 --- a/giant/config.py +++ b/giant/config.py @@ -893,6 +893,114 @@ def _deep_merge(base: dict, override: dict) -> dict: return result +@dataclass(frozen=True) +class FlagSpec: + """One CLI flag's mapping into the config-overrides tree. + + `paths` lists every dotted config path this flag writes (>1 means fan-out + to multiple stages/axes, e.g. `--mode` -> both stages' `generator`). + `precedence` controls write order when two flags target the same path: + specs are applied in ascending precedence, so a higher-precedence (more + specific) flag overwrites a lower-precedence (shared/shorthand) one — + this is the "build a shared dict, then let a more specific dict win" + pattern `giant train`/`giant new-run` need (e.g. `--hidden-dim` vs + `--stage1-hidden-dim`, or `--n-critic` vs `--stage1-n-critic`), + generalized to one mechanism instead of three different ad hoc ones. + """ + + name: str + paths: tuple[str, ...] + precedence: int = 0 + + +# Flag -> config-path table shared by `giant train`/`giant new-run` +# (giant/cli.py) so both commands resolve CLI overrides identically. See +# issues.md Issue 3: this replaces ~140 lines of hand-written, imperative +# dict-building in cli.py with one declarative table plus +# `overrides_from_flags` below. +FLAG_SPECS: tuple[FlagSpec, ...] = ( + # train block -- flat pass-through, unique paths, precedence irrelevant. + FlagSpec("epochs", ("train.epochs",)), + FlagSpec("batch_size", ("train.batch_size",)), + FlagSpec("lr", ("train.lr",)), + FlagSpec("weight_decay", ("train.weight_decay",)), + FlagSpec("ema_decay", ("train.ema_decay",)), + FlagSpec("warmup_epochs", ("train.warmup_epochs",)), + FlagSpec("val_fraction", ("train.val_fraction",)), + FlagSpec("num_workers", ("train.num_workers",)), + FlagSpec("seed", ("train.seed",)), + FlagSpec("validate_every", ("train.validate_every",)), + FlagSpec("validate_steps", ("train.validate_steps",)), + FlagSpec("max_val_batches", ("train.max_val_batches",)), + FlagSpec("wandb", ("train.wandb",)), + FlagSpec("wandb_project", ("train.wandb_project",)), + FlagSpec("wandb_run_name", ("train.wandb_run_name",)), + FlagSpec("wandb_log_every", ("train.wandb_log_every",)), + # --hidden-dim/--n-blocks/--dropout are stage-1-only backward-compat + # shorthands (they predate stage2_model having its own flags); + # --stage1-* wins when both are given. + FlagSpec("hidden_dim", ("stage1_model.hidden_dim",), precedence=0), + FlagSpec("stage1_hidden_dim", ("stage1_model.hidden_dim",), precedence=1), + FlagSpec("n_blocks", ("stage1_model.n_res_blocks",), precedence=0), + FlagSpec("stage1_n_res_blocks", ("stage1_model.n_res_blocks",), precedence=1), + FlagSpec("dropout", ("stage1_model.dropout",), precedence=0), + FlagSpec("stage1_dropout", ("stage1_model.dropout",), precedence=1), + # stage2-only knobs. + FlagSpec("stage2_hidden_dim", ("stage2_model.hidden_dim",)), + FlagSpec("stage2_n_res_blocks", ("stage2_model.n_res_blocks",)), + FlagSpec("stage2_dropout", ("stage2_model.dropout",)), + FlagSpec("stage2_decoder", ("stage2_model.decoder",)), + FlagSpec("stage2_k_max", ("stage2_model.k_max",)), + FlagSpec("stage2_context_dim", ("stage2_model.context_dim",)), + FlagSpec("stage2_stage1_context", ("stage2_model.stage1_context",)), + # --mode applies to both stages by default (v0.2 had one shared + # mode/wgan config); --stage{1,2}-generator override a single stage. + FlagSpec("mode", ("stage1_model.generator", "stage2_model.generator"), precedence=0), + FlagSpec("stage1_generator", ("stage1_model.generator",), precedence=1), + FlagSpec("stage2_generator", ("stage2_model.generator",), precedence=1), + # --emb-dim/--conditioning set both conditioning axes (v0.2 had one + # shared value for particle+material). + FlagSpec("conditioning", ("conditioning.particle.type", "conditioning.material.type")), + FlagSpec("emb_dim", ("conditioning.particle.emb_dim", "conditioning.material.emb_dim")), + # Pre-aggregated router override dict (built by `_router_cli_overrides` + # in cli.py from --router/--router-type/--n-experts/--router-axis). + # Router overrides only ever land on stage1_model -- this asymmetry is + # deliberate (see cli.py) and must not be "fixed" into a fan-out here. + FlagSpec("router_config", ("stage1_model.router",)), + # WGAN: shared knobs apply to both stages by default (v0.2 had one + # shared wgan config); --stage{1,2}-* override a single stage. + FlagSpec("n_critic", ("stage1_model.wgan.n_critic", "stage2_model.wgan.n_critic"), precedence=0), + FlagSpec("stage1_n_critic", ("stage1_model.wgan.n_critic",), precedence=1), + FlagSpec("stage2_n_critic", ("stage2_model.wgan.n_critic",), precedence=1), + FlagSpec("gp_weight", ("stage1_model.wgan.gp_weight", "stage2_model.wgan.gp_weight"), precedence=0), + FlagSpec("stage1_gp_weight", ("stage1_model.wgan.gp_weight",), precedence=1), + FlagSpec("stage2_gp_weight", ("stage2_model.wgan.gp_weight",), precedence=1), + FlagSpec("noise_dim", ("stage1_model.wgan.noise_dim", "stage2_model.wgan.noise_dim"), precedence=0), + FlagSpec("stage1_noise_dim", ("stage1_model.wgan.noise_dim",), precedence=1), + FlagSpec("stage2_noise_dim", ("stage2_model.wgan.noise_dim",), precedence=1), + FlagSpec("critic_lr", ("stage1_model.wgan.critic_lr", "stage2_model.wgan.critic_lr"), precedence=0), + FlagSpec("stage1_critic_lr", ("stage1_model.wgan.critic_lr",), precedence=1), + FlagSpec("stage2_critic_lr", ("stage2_model.wgan.critic_lr",), precedence=1), +) + + +def overrides_from_flags(values: dict[str, object]) -> dict: + """Build the nested, section-keyed config-overrides dict + `merge_cli_overrides` expects, from `{flag_name: value}`. + + Flags absent from `values`, or mapped to `None` (= not given on the + CLI), are skipped. See `FlagSpec`/`FLAG_SPECS` above for the precedence + rule applied when two flags target the same path. + """ + overrides: dict = {} + for spec in sorted(FLAG_SPECS, key=lambda s: s.precedence): + if spec.name not in values or values[spec.name] is None: + continue + for path in spec.paths: + _set_path(overrides, path, values[spec.name]) + return overrides + + # v0.2 [train] keys that pass through to v0.3 [train] unchanged (same name, # same meaning) when present in the loaded file — everything model-shaped # moved to the stage/conditioning blocks instead (see the rest of diff --git a/tests/test_cli_train_overrides.py b/tests/test_cli_train_overrides.py index 486ea4b..7df258f 100644 --- a/tests/test_cli_train_overrides.py +++ b/tests/test_cli_train_overrides.py @@ -94,3 +94,83 @@ def test_wgan_knobs_split_per_stage(monkeypatch, tmp_path): assert cfg["stage1_model"]["wgan"]["gp_weight"] == 10.0 assert cfg["stage2_model"]["wgan"]["n_critic"] == 5 assert cfg["stage2_model"]["wgan"]["gp_weight"] == 2.5 + + +def test_batch_size_invalid_string_errors(monkeypatch, tmp_path): + monkeypatch.setattr(cli, "run_train_job", lambda *a, **kw: None) + result = runner.invoke( + cli.app, + ["train", "dummy.parquet", "--out", str(tmp_path / "run"), "--batch-size", "not-a-number"], + ) + assert result.exit_code == 1 + assert "--batch-size must be an integer or 'auto'" in result.output + + +def test_out_dir_resolution_prefers_explicit_out_over_resume(monkeypatch, tmp_path): + captured: dict = {} + + def _fake_run_train_job(*, data, cfg, out_dir, **kwargs): + captured["out_dir"] = out_dir + + monkeypatch.setattr(cli, "run_train_job", _fake_run_train_job) + + resume_dir = tmp_path / "resumed_run" + resume_dir.mkdir() + (resume_dir / "last.pt").touch() + explicit_out = tmp_path / "explicit_run" + + result = runner.invoke( + cli.app, + ["train", "dummy.parquet", "--out", str(explicit_out), "--resume", str(resume_dir / "last.pt")], + ) + assert result.exit_code == 0, result.output + assert captured["out_dir"] == explicit_out + + +def test_out_dir_resolution_falls_back_to_resume_parent(monkeypatch, tmp_path): + captured: dict = {} + + def _fake_run_train_job(*, data, cfg, out_dir, **kwargs): + captured["out_dir"] = out_dir + + monkeypatch.setattr(cli, "run_train_job", _fake_run_train_job) + + resume_dir = tmp_path / "resumed_run" + resume_dir.mkdir() + (resume_dir / "last.pt").touch() + + result = runner.invoke(cli.app, ["train", "dummy.parquet", "--resume", str(resume_dir / "last.pt")]) + assert result.exit_code == 0, result.output + assert captured["out_dir"] == resume_dir + + +def test_out_dir_resolution_defaults_when_neither_out_nor_resume_given(monkeypatch, tmp_path): + captured: dict = {} + + def _fake_run_train_job(*, data, cfg, out_dir, **kwargs): + captured["out_dir"] = out_dir + + monkeypatch.setattr(cli, "run_train_job", _fake_run_train_job) + monkeypatch.chdir(tmp_path) + + result = runner.invoke(cli.app, ["train", "dummy.parquet"]) + assert result.exit_code == 0, result.output + assert captured["out_dir"] == Path("checkpoints") / cli.gconfig.default_out_dir_name(cli.gconfig.DEFAULT_CONFIG) + + +def test_batch_size_auto_estimates_and_echoes(monkeypatch, tmp_path): + captured: dict = {} + + def _fake_run_train_job(*, data, cfg, out_dir, num_workers, **kwargs): + captured["batch_size"] = cfg["train"]["batch_size"] + + monkeypatch.setattr(cli, "run_train_job", _fake_run_train_job) + monkeypatch.setattr(cli.gconfig, "estimate_batch_size", lambda hidden_dim, n_blocks, device: 123) + + result = runner.invoke( + cli.app, + ["train", "dummy.parquet", "--out", str(tmp_path / "run"), "--batch-size", "auto"], + ) + assert result.exit_code == 0, result.output + assert captured["batch_size"] == 123 + assert "batch_size: 123 (auto-estimated from free GPU memory)" in result.output diff --git a/tests/test_config.py b/tests/test_config.py index ceb5e73..58d4d1c 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -889,6 +889,125 @@ def test_merge_cli_overrides_real_config_fixtures_pass_key_validation(fixture_na gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, _CONFIGS_DIR / fixture_name, {}) # must not raise +# --------------------------------------------------------------------------- +# overrides_from_flags (issues.md Issue 3): the flag -> config-path table +# shared by `giant train`/`giant new-run`. Each test below pins one +# precedence rule directly, without CliRunner — see also +# tests/test_cli_train_overrides.py for the thin end-to-end smoke coverage. +# --------------------------------------------------------------------------- + + +def test_overrides_from_flags_empty_values_yield_empty_overrides(): + assert gconfig.overrides_from_flags({}) == {} + assert gconfig.overrides_from_flags({"epochs": None, "hidden_dim": None}) == {} + + +def test_overrides_from_flags_train_block_passthrough(): + overrides = gconfig.overrides_from_flags({"epochs": 5, "lr": 1e-3, "hidden_dim": None}) + assert overrides == {"train": {"epochs": 5, "lr": 1e-3}} + + +@pytest.mark.parametrize( + ("shorthand", "explicit", "path_key"), + [ + ("hidden_dim", "stage1_hidden_dim", "hidden_dim"), + ("n_blocks", "stage1_n_res_blocks", "n_res_blocks"), + ("dropout", "stage1_dropout", "dropout"), + ], +) +def test_overrides_from_flags_stage1_explicit_overrides_shorthand(shorthand, explicit, path_key): + overrides = gconfig.overrides_from_flags({shorthand: 1, explicit: 2}) + assert overrides["stage1_model"][path_key] == 2 + + +@pytest.mark.parametrize( + ("shorthand", "path_key"), + [("hidden_dim", "hidden_dim"), ("n_blocks", "n_res_blocks"), ("dropout", "dropout")], +) +def test_overrides_from_flags_stage1_shorthand_alone(shorthand, path_key): + overrides = gconfig.overrides_from_flags({shorthand: 7}) + assert overrides["stage1_model"][path_key] == 7 + + +def test_overrides_from_flags_stage2_only_knobs(): + overrides = gconfig.overrides_from_flags( + { + "stage2_hidden_dim": 32, + "stage2_n_res_blocks": 4, + "stage2_dropout": 0.1, + "stage2_decoder": "one_shot", + "stage2_k_max": 8, + "stage2_context_dim": 16, + "stage2_stage1_context": "sampled", + } + ) + assert overrides["stage2_model"] == { + "hidden_dim": 32, + "n_res_blocks": 4, + "dropout": 0.1, + "decoder": "one_shot", + "k_max": 8, + "context_dim": 16, + "stage1_context": "sampled", + } + assert "stage1_model" not in overrides + + +def test_overrides_from_flags_mode_fans_to_both_stages(): + overrides = gconfig.overrides_from_flags({"mode": "wgan"}) + assert overrides["stage1_model"]["generator"] == "wgan" + assert overrides["stage2_model"]["generator"] == "wgan" + + +def test_overrides_from_flags_stage1_generator_overrides_mode_for_stage1_only(): + overrides = gconfig.overrides_from_flags({"mode": "wgan", "stage1_generator": "flow"}) + assert overrides["stage1_model"]["generator"] == "flow" + assert overrides["stage2_model"]["generator"] == "wgan" + + +def test_overrides_from_flags_stage2_generator_overrides_mode_for_stage2_only(): + overrides = gconfig.overrides_from_flags({"mode": "wgan", "stage2_generator": "flow"}) + assert overrides["stage1_model"]["generator"] == "wgan" + assert overrides["stage2_model"]["generator"] == "flow" + + +def test_overrides_from_flags_emb_dim_sets_both_conditioning_axes(): + overrides = gconfig.overrides_from_flags({"emb_dim": 24}) + assert overrides["conditioning"]["particle"]["emb_dim"] == 24 + assert overrides["conditioning"]["material"]["emb_dim"] == 24 + + +def test_overrides_from_flags_conditioning_sets_both_axes_type(): + overrides = gconfig.overrides_from_flags({"conditioning": "onehot"}) + assert overrides["conditioning"]["particle"]["type"] == "onehot" + assert overrides["conditioning"]["material"]["type"] == "onehot" + + +def test_overrides_from_flags_router_config_only_touches_stage1(): + overrides = gconfig.overrides_from_flags({"router_config": {"enabled": True, "type": "energy"}}) + assert overrides["stage1_model"]["router"] == {"enabled": True, "type": "energy"} + assert "stage2_model" not in overrides + + +@pytest.mark.parametrize( + ("shared", "stage1_specific", "stage2_specific", "path_key"), + [ + ("n_critic", "stage1_n_critic", "stage2_n_critic", "n_critic"), + ("gp_weight", "stage1_gp_weight", "stage2_gp_weight", "gp_weight"), + ("noise_dim", "stage1_noise_dim", "stage2_noise_dim", "noise_dim"), + ("critic_lr", "stage1_critic_lr", "stage2_critic_lr", "critic_lr"), + ], +) +def test_overrides_from_flags_wgan_knobs_split_per_stage(shared, stage1_specific, stage2_specific, path_key): + overrides = gconfig.overrides_from_flags({shared: 5.0, stage1_specific: 3.0}) + assert overrides["stage1_model"]["wgan"][path_key] == 3.0 + assert overrides["stage2_model"]["wgan"][path_key] == 5.0 + + overrides = gconfig.overrides_from_flags({shared: 5.0, stage2_specific: 2.5}) + assert overrides["stage1_model"]["wgan"][path_key] == 5.0 + assert overrides["stage2_model"]["wgan"][path_key] == 2.5 + + # --------------------------------------------------------------------------- # checkpoint config-mismatch warnings (unchanged surface, still exercised) # --------------------------------------------------------------------------- -- 2.39.5 From 6a21c3b908cbb86e14e774b154a2d8993718f7a4 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 15:05:33 +0200 Subject: [PATCH 28/30] Mark issues.md Issues 3 & 4 as fixed Records what commit 2bfb1ab actually changed and its scope, matching the status-blockquote convention already used for Issues 1 and 2. Co-Authored-By: Claude Sonnet 5 --- issues.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 47 insertions(+), 2 deletions(-) diff --git a/issues.md b/issues.md index abdcd5d..7684edf 100644 --- a/issues.md +++ b/issues.md @@ -42,8 +42,8 @@ architecture matrix grows, not about rot or breakage. |---|---|---|---|---| | 1 | Config defaults are declared twice; `DEFAULT_CONFIG` and consumers already disagree | **High** | Medium | **Fixed** (`9bf5874`) | | 2 | No unknown-key validation — a typo in `config.toml` silently trains the wrong model | **High** | Small | **Fixed** | -| 3 | `cli.py:train()` is a 58-parameter, 510-line fat controller | **High** | Medium | Open | -| 4 | `cli.py` is at 35.8 % coverage and holds untested override-precedence logic | **High** | Medium | Open | +| 3 | `cli.py:train()` is a 58-parameter, 510-line fat controller | **High** | Medium | **Fixed** (`2bfb1ab`) | +| 4 | `cli.py` is at 35.8 % coverage and holds untested override-precedence logic | **High** | Medium | **Fixed** (`2bfb1ab`, partial — see status note) | | 5 | Inference bootstrap is duplicated verbatim between `predict` and `rollout` | **High** | Small | Open | | 6 | Two independent v0.2→v0.3 migration surfaces encode the same knowledge | Medium | Medium | Open | | 7 | Positional tuple contracts (9-tuple, 7-tuple) between data, model and training layers | Medium | Small | Open | @@ -373,6 +373,26 @@ rollout` against every model trained so far. ## Issue 3 — `cli.py:train()` is a 58-parameter, 510-line fat controller +> **Status: Fixed, commit `2bfb1ab` on `v0.3.0-stage2-autoregressive`.** `giant/config.py` +> now declares `FlagSpec` (a frozen dataclass: `name`, `paths` — one or more dotted config +> paths, `precedence`) and a `FLAG_SPECS` table covering every flag `train`/`new-run` map +> into the config tree, plus `overrides_from_flags(values: dict[str, object]) -> dict`, +> which applies specs in ascending precedence order (so a more-specific flag overwrites a +> shared/shorthand one written earlier at the same path) — one mechanism replacing the +> three different ad hoc "more specific wins" patterns identified below +> (`.update()`-call-order, `setdefault(...)[...] =` overwrite-order, and +> `{**shared, **specific}` merge). `train()`'s ~140-line override-building block +> (`cli.py:656-790` pre-fix) is now a single flat `flag_values` dict (mostly enum `.value` +> unwrapping) plus one call to `overrides_from_flags`; `new_run()`'s near-verbatim copy +> (`cli.py:915-983` pre-fix) collapsed the same way, reusing the identical table. Router +> overrides (`_router_cli_overrides`, unchanged, still shared by both commands) feed into +> the table as a single pre-aggregated `router_config` entry mapped only to +> `stage1_model.router` — the stage1-only asymmetry noted below is preserved exactly, with +> a regression test. No flag was added, removed, or renamed, and no precedence semantics +> changed: `giant train --help`/`giant new-run --help` are byte-identical before and after, +> verified by diffing both. Everything below this point describes the pre-fix state and is +> kept for historical context. + **Severity: High. Effort: Medium.** **Location:** `giant/cli.py:329-839`. @@ -488,6 +508,31 @@ separate inline comment blocks to reconstruct. ## Issue 4 — `cli.py` sits at 35.8 % coverage and holds untested override-precedence logic +> **Status: Fixed for the override-precedence logic (Issue 3's scope); the +> `predict`/`rollout` inference-bootstrap portion described below is still open — that is +> Issue 5, deliberately not attempted here.** Commit `2bfb1ab` on +> `v0.3.0-stage2-autoregressive` adds direct, `CliRunner`-free unit tests for +> `overrides_from_flags` in `tests/test_config.py` — one per precedence rule, including +> several with previously **zero** coverage: both legs of `--stage{1,2}-generator` +> overriding `--mode` (only the stage1 leg had a test before), all three stage1 +> shorthand-vs-`--stage1-*` pairs (previously only `hidden_dim`), all four WGAN knobs' +> shared-vs-per-stage precedence (previously only `n_critic`/`gp_weight`), the +> `--emb-dim`/`--conditioning` dual-axis fan-out (previously untested via `train` at all), +> and an explicit regression test that `router_config` only ever writes +> `stage1_model.router`. `tests/test_cli_train_overrides.py` keeps its original 4 +> `CliRunner` smoke tests unmodified, plus 4 new ones covering the `--batch-size auto` +> parse-error and success paths and all three `out_dir` resolution branches +> (`--out`/`--resume`/default) — previously entirely uncovered. The percentage barely moves +> (36 % → 35 %), because the extraction *deleted* more statements from `cli.py` (539→478) +> than the new tests cover elsewhere in the file, but covered statements rose in absolute +> terms (157→169) and every override-precedence line the original issue called out by +> number is now covered. Coverage of `cli.py`'s other listed gap — the `predict`/ +> `rollout` inference bootstrap (`cli.py:1104-1423`/`1528-1702` in the pre-fix numbering) — +> is unchanged, since fixing that requires the `load_for_inference` extraction described in +> Issue 5, which is explicitly out of scope for this fix (done separately, if at all). +> Everything below this point describes the pre-fix state and is kept for historical +> context. + **Severity: High. Effort: Medium.** **Location:** `giant/cli.py` (1865 lines). -- 2.39.5 From 818c380fd016b056a18223ea8a6db46eb96d845a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 15:31:14 +0200 Subject: [PATCH 29/30] Extract predict/rollout's duplicated inference bootstrap into giant.checkpoint_io (issues.md Issue 5) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit giant predict and giant rollout each carried a ~65-line, independently drifting copy of "load checkpoint -> validate -> resolve conditioning axes -> restore normalizers/vocab maps -> build models -> load weights", plus a third partial copy of _conditioning_axes in analysis/router_gating.py. A silent divergence there doesn't crash, it makes the two commands run different physics from the same checkpoint with no test coverage anywhere along that path. giant/checkpoint_io.py now holds the single implementation: load_for_inference() + an InferenceContext dataclass, raising CheckpointCompatibilityError (verbatim message text preserved) instead of calling typer directly, so it can be unit-tested and imported from non-Typer code. router_gating.py's load_router imports conditioning_axes from it lazily, keeping its "no torch at module scope" contract intact. Adds 17 direct unit tests for load_for_inference/conditioning_axes/stage_cfg plus CLI smoke tests confirming the error surfaces as typer.Exit(1) through predict and rollout — previously zero coverage on this path. Co-Authored-By: Claude Sonnet 5 --- giant/analysis/router_gating.py | 20 +-- giant/checkpoint_io.py | 208 +++++++++++++++++++++++++++ giant/cli.py | 244 +++++--------------------------- tests/test_checkpoint_io.py | 236 ++++++++++++++++++++++++++++++ tests/test_cli_predict.py | 22 +++ tests/test_cli_rollout.py | 33 +++++ 6 files changed, 539 insertions(+), 224 deletions(-) create mode 100644 giant/checkpoint_io.py create mode 100644 tests/test_checkpoint_io.py create mode 100644 tests/test_cli_rollout.py diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index a13c287..2a6a70f 100644 --- a/giant/analysis/router_gating.py +++ b/giant/analysis/router_gating.py @@ -66,27 +66,11 @@ class _RouterHandle: router_type: str -def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str, str]: - """(particle_conditioning, material_conditioning) for - `giant.data.transforms.build_cond_features` — from either a v0.2 - checkpoint's flat `model_config["conditioning"]` (one shared string, same - for both axes) or a new-format one (independent - `model_config["conditioning"]["particle"/"material"]["type"]` — the two - axes may differ). Mirrors - `giant.cli._conditioning_axes`.""" - raw = model_cfg.get("conditioning", default) - if isinstance(raw, dict): - return ( - raw.get("particle", {}).get("type", default), - raw.get("material", {}).get("type", default), - ) - return raw, raw - - def load_router(checkpoint: str | Path) -> _RouterHandle | None: """Load a checkpoint's Stage-1 router, or None if it isn't a MoE checkpoint.""" import torch + from giant.checkpoint_io import conditioning_axes from giant.data.transforms import Normalizer from giant.model.network import build_models @@ -110,7 +94,7 @@ def load_router(checkpoint: str | Path) -> _RouterHandle | None: if router is None: return None - particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) + particle_conditioning, material_conditioning = conditioning_axes(model_cfg) return _RouterHandle( router=router, pdg_map={int(k): v for k, v in ckpt["pdg_map"].items()}, diff --git a/giant/checkpoint_io.py b/giant/checkpoint_io.py new file mode 100644 index 0000000..4ee92cc --- /dev/null +++ b/giant/checkpoint_io.py @@ -0,0 +1,208 @@ +"""Load a trained checkpoint into ready-to-run models (giant.cli's `predict`/`rollout`). + +Both commands need the same ~15 steps to go from a checkpoint path to two +`eval()`-mode models plus their normalizers/vocab maps: load the pickle, +validate it carries what current code expects, resolve which conditioning +mode each axis was trained with, restore the top-N vocab maps (if the +checkpoint used one-hot conditioning), rebuild the normalizers, construct the +model from `model_config`, and load the requested (raw or EMA) weights. This +used to be duplicated near-verbatim in both commands (issues.md Issue 5) — +`load_for_inference` is the single implementation. + +This module intentionally has no Typer dependency, so it can be unit-tested +directly and imported from non-CLI code (`giant.analysis.router_gating`, +lazily — see that module's docstring for why). Failures raise +`CheckpointCompatibilityError` with the same wording the CLI has always +shown; the CLI layer catches it and does the `typer.echo`/`Exit(1)`. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +import torch +from torch import nn + +from giant import config as gconfig +from giant.constants import K_MAX +from giant.data.loader import TopNMap +from giant.data.setup_cache import topnmap_from_json +from giant.data.transforms import Normalizer +from giant.model.network import build_models + + +class CheckpointCompatibilityError(Exception): + """Checkpoint is missing something `load_for_inference` needs.""" + + +def conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str, str]: + """(particle_conditioning, material_conditioning) for + `giant.data.transforms.build_cond_features`/`build_features` — from + either a v0.2 checkpoint's flat `model_config["conditioning"]` (one + shared string, same for both axes) or a new-format one (independent + `model_config["conditioning"]["particle"/"material"]["type"]` — the two + axes are configured independently and may differ).""" + raw = model_cfg.get("conditioning", default) + if isinstance(raw, dict): + return ( + raw.get("particle", {}).get("type", default), + raw.get("material", {}).get("type", default), + ) + return raw, raw + + +def stage_cfg(model_cfg: dict, stage: str) -> dict: + """`model_cfg[f"{stage}_model"]` for a new-format model_config, `{}` for + a v0.2 flat one (whose ddpm schedule always used `CosineSchedule`'s own + default `T=1000` — never a config key — and which never had + `particle_type` at all, so `{}` is the correct fallback for both + `ddpm_steps`/`particle_type_other_policy` below).""" + val = model_cfg.get(f"{stage}_model") + return val if isinstance(val, dict) else {} + + +def ddpm_steps(model_cfg: dict, stage: str) -> int: + return stage_cfg(model_cfg, stage).get("ddpm", {}).get("n_steps", 1000) + + +def particle_type_other_policy(model_cfg: dict) -> str: + return stage_cfg(model_cfg, "stage2").get("particle_type", {}).get("other_policy", "sample") + + +def load_pdg_topn_map(ckpt: dict) -> TopNMap | None: + """`ckpt["pdg_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if + this checkpoint's conditioning/particle_type never needed one (see + `giant.pipeline.run_setup_stage`, which only populates it when + `conditioning.particle.type` or `stage2_model.particle_type.target` is + `"onehot"`).""" + raw = ckpt.get("pdg_topn_map") + return topnmap_from_json(raw, axis="pdg") if raw is not None else None + + +def load_mat_topn_map(ckpt: dict) -> TopNMap | None: + """`ckpt["mat_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if + this checkpoint's `conditioning.material.type` was never `"onehot"` (see + `giant.pipeline.run_setup_stage`).""" + raw = ckpt.get("mat_topn_map") + return topnmap_from_json(raw, axis="material") if raw is not None else None + + +@dataclass(frozen=True) +class InferenceContext: + """Everything needed to run a trained checkpoint forward, resolved once.""" + + stage1: nn.Module | None + stage2: nn.Module | None + cond_norm: Normalizer + tgt_norm: Normalizer + sec_phys_norm: Normalizer + pdg_map: dict[int, int] + mat_map: dict[str, int] + pdg_topn_map: TopNMap | None + mat_topn_map: TopNMap | None + particle_conditioning: str + material_conditioning: str + k_max: int + stage1_ddpm_steps: int + stage2_ddpm_steps: int + other_policy: str + model_config: dict + epoch: int | None + best_val_loss: float | None + + +def load_for_inference( + checkpoint: Path, + device: torch.device, + command_name: str, + weights: str = "raw", + require_stage2: bool = True, +) -> InferenceContext: + """Load *checkpoint* and reconstruct everything `predict`/`rollout` need + to run it forward, on *device*, in `eval()` mode. + + *command_name* (e.g. `"predict"`/`"rollout"`) only feeds the "needs both" + error message below. *weights* is `"raw"` (the live training weights) or + `"ema"` (the EMA shadow copy, see `--ema-decay`). *require_stage2* + controls whether a checkpoint with an inactive stage 2 + (`stage2_model.active = false`) is an error (both current callers need + both stages) or an acceptable `stage2 = None` result — kept as a real + parameter since `stage{1,2}_model.active` is a real, if currently + stage1+stage2-only-in-practice, config option. + """ + ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) + for key in ("model_config", "sec_decoder"): + if key not in ckpt: + raise CheckpointCompatibilityError(f"checkpoint has no {key} — retrain with the current code") + + if "sec_phys" not in ckpt.get("normalizer", {}): + raise CheckpointCompatibilityError("checkpoint has no normalizer.sec_phys — retrain with the current code") + + gconfig.warn_if_checkpoint_config_mismatch(checkpoint) + + model_cfg = ckpt["model_config"] + particle_conditioning, material_conditioning = conditioning_axes(model_cfg) + pdg_topn_map = load_pdg_topn_map(ckpt) + mat_topn_map = load_mat_topn_map(ckpt) + if particle_conditioning == "onehot" and pdg_topn_map is None: + raise CheckpointCompatibilityError( + "checkpoint's conditioning.particle.type='onehot' but has no pdg_topn_map — retrain with the current code" + ) + if material_conditioning == "onehot" and mat_topn_map is None: + raise CheckpointCompatibilityError( + "checkpoint's conditioning.material.type='onehot' but has no mat_topn_map — retrain with the current code" + ) + other_policy = particle_type_other_policy(model_cfg) + stage1_ddpm_steps = ddpm_steps(model_cfg, "stage1") + stage2_ddpm_steps = ddpm_steps(model_cfg, "stage2") + k_max = stage_cfg(model_cfg, "stage2").get("k_max", K_MAX) + pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} + mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} + cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) + tgt_norm = Normalizer.from_dict(ckpt["normalizer"]["target"]) + sec_phys_norm = Normalizer.from_dict(ckpt["normalizer"]["sec_phys"]) + + built = build_models(model_cfg) + stage1, stage2 = built["stage1"], built["stage2"] + if require_stage2 and (stage1 is None or stage2 is None): + raise CheckpointCompatibilityError( + f"checkpoint has an inactive stage1 or stage2 — {command_name} needs both (see stage{{1,2}}_model.active)" + ) + + if weights == "raw": + model_key, sec_key = "model", "sec_decoder" + else: + model_key, sec_key = "model_ema", "sec_decoder_ema" + if model_key not in ckpt or sec_key not in ckpt: + raise CheckpointCompatibilityError( + f"{checkpoint} has no EMA weights (trained before --ema-decay, " + "or with --ema-decay 0) — use --weights raw" + ) + if stage1 is not None: + stage1.load_state_dict(ckpt[model_key]) + stage1.to(device).eval() + if stage2 is not None: + stage2.load_state_dict(ckpt[sec_key]) + stage2.to(device).eval() + + return InferenceContext( + stage1=stage1, + stage2=stage2, + cond_norm=cond_norm, + tgt_norm=tgt_norm, + sec_phys_norm=sec_phys_norm, + pdg_map=pdg_map, + mat_map=mat_map, + pdg_topn_map=pdg_topn_map, + mat_topn_map=mat_topn_map, + particle_conditioning=particle_conditioning, + material_conditioning=material_conditioning, + k_max=k_max, + stage1_ddpm_steps=stage1_ddpm_steps, + stage2_ddpm_steps=stage2_ddpm_steps, + other_policy=other_policy, + model_config=model_cfg, + epoch=ckpt.get("epoch"), + best_val_loss=ckpt.get("best_val_loss"), + ) diff --git a/giant/cli.py b/giant/cli.py index 533034c..ea254bb 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -19,7 +19,6 @@ from tqdm import tqdm from giant import config as gconfig from giant.constants import ( - K_MAX, LOCAL_TARGET_NAMES, PREDICT_COORD_METADATA_KEY, PREDICT_SCHEMA_VERSION, @@ -39,11 +38,9 @@ from giant.data.transforms import ( inv_local_frame_rotation, inv_log_transform, reconstruct_post_pos, - Normalizer, ) -from giant.data.setup_cache import topnmap_from_json +from giant.checkpoint_io import CheckpointCompatibilityError, load_for_inference from giant.geometry import GeometryOracle -from giant.model.network import build_models from giant.pipeline import run_train_job from giant.rollout import ( L1DistCollector, @@ -71,58 +68,6 @@ def _router_total_experts(router_cfg: dict) -> int: return int(router_cfg.get("n_experts", 1)) -def _conditioning_axes(model_cfg: dict, default: str = "embedding") -> tuple[str, str]: - """(particle_conditioning, material_conditioning) for - `giant.data.transforms.build_cond_features`/`build_features` — from - either a v0.2 checkpoint's flat `model_config["conditioning"]` (one - shared string, same for both axes) or a new-format one (independent - `model_config["conditioning"]["particle"/"material"]["type"]` — the two - axes are configured independently and may differ).""" - raw = model_cfg.get("conditioning", default) - if isinstance(raw, dict): - return ( - raw.get("particle", {}).get("type", default), - raw.get("material", {}).get("type", default), - ) - return raw, raw - - -def _stage_cfg(model_cfg: dict, stage: str) -> dict: - """`model_cfg[f"{stage}_model"]` for a new-format model_config, `{}` for - a v0.2 flat one (whose ddpm schedule always used `CosineSchedule`'s own - default `T=1000` — never a config key — and which never had - `particle_type` at all, so `{}` is the correct fallback for both - `_ddpm_steps`/`_particle_type_other_policy` below).""" - val = model_cfg.get(f"{stage}_model") - return val if isinstance(val, dict) else {} - - -def _ddpm_steps(model_cfg: dict, stage: str) -> int: - return _stage_cfg(model_cfg, stage).get("ddpm", {}).get("n_steps", 1000) - - -def _particle_type_other_policy(model_cfg: dict) -> str: - return _stage_cfg(model_cfg, "stage2").get("particle_type", {}).get("other_policy", "sample") - - -def _load_pdg_topn_map(ckpt: dict): - """`ckpt["pdg_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if - this checkpoint's conditioning/particle_type never needed one (see - `giant.pipeline.run_setup_stage`, which only populates it when - `conditioning.particle.type` or `stage2_model.particle_type.target` is - `"onehot"`).""" - raw = ckpt.get("pdg_topn_map") - return topnmap_from_json(raw, axis="pdg") if raw is not None else None - - -def _load_mat_topn_map(ckpt: dict): - """`ckpt["mat_topn_map"]` as a `giant.data.loader.TopNMap`, or `None` if - this checkpoint's `conditioning.material.type` was never `"onehot"` (see - `giant.pipeline.run_setup_stage`).""" - raw = ckpt.get("mat_topn_map") - return topnmap_from_json(raw, axis="material") if raw is not None else None - - def _batch_size_estimate_dims(model_cfg: dict, training: bool, stage: str = "stage1") -> tuple[int, int]: """Pick the (hidden_dim, n_blocks) that dominate per-call activation memory. @@ -298,34 +243,6 @@ class Weights(str, Enum): ema = "ema" -def _load_model_weights( - model: torch.nn.Module, - sec_decoder: torch.nn.Module, - ckpt: dict, - weights: "Weights", - checkpoint_path: Path, -) -> None: - """Load either the raw or EMA state dicts from a training checkpoint. - - EMA weights (giant.training's shadow copy, see --ema-decay) only exist in - checkpoints written after that feature landed, so `ema` fails loudly - rather than silently falling back to raw weights a caller didn't ask for. - """ - if weights == Weights.raw: - model_key, sec_key = "model", "sec_decoder" - else: - model_key, sec_key = "model_ema", "sec_decoder_ema" - if model_key not in ckpt or sec_key not in ckpt: - typer.echo( - f"error: {checkpoint_path} has no EMA weights (trained before " - "--ema-decay, or with --ema-decay 0) — use --weights raw", - err=True, - ) - raise typer.Exit(1) - model.load_state_dict(ckpt[model_key]) - sec_decoder.load_state_dict(ckpt[sec_key]) - - @app.command() def train( data: Annotated[Path, typer.Argument(help="Parquet file or directory of parquet files")], @@ -992,37 +909,25 @@ def predict( typer.echo(f"device: {_device}") # --- Load checkpoint --- - ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) - if "model_config" not in ckpt: - typer.echo( - "error: checkpoint has no model_config — retrain with the current code", - err=True, - ) + try: + ctx = load_for_inference(checkpoint, _device, "predict", weights=weights.value) + except CheckpointCompatibilityError as exc: + typer.echo(f"error: {exc}", err=True) raise typer.Exit(1) + typer.echo(f"loaded checkpoint: {checkpoint} (weights: {weights.value})") - if "sec_decoder" not in ckpt: - typer.echo( - "error: checkpoint has no sec_decoder — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - - if "sec_phys" not in ckpt.get("normalizer", {}): - typer.echo( - "error: checkpoint has no normalizer.sec_phys — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - - model_cfg = ckpt["model_config"] - pdg_topn_map = _load_pdg_topn_map(ckpt) - mat_topn_map = _load_mat_topn_map(ckpt) - other_policy = _particle_type_other_policy(model_cfg) - stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") - stage2_k_max = _stage_cfg(model_cfg, "stage2").get("k_max", K_MAX) + assert ctx.stage1 is not None and ctx.stage2 is not None # require_stage2=True (default) guarantees this + model, sec_decoder = ctx.stage1, ctx.stage2 + cond_norm, tgt_norm, sec_phys_norm = ctx.cond_norm, ctx.tgt_norm, ctx.sec_phys_norm + pdg_map, mat_map = ctx.pdg_map, ctx.mat_map + pdg_topn_map, mat_topn_map = ctx.pdg_topn_map, ctx.mat_topn_map + particle_conditioning, material_conditioning = ctx.particle_conditioning, ctx.material_conditioning + other_policy = ctx.other_policy + stage1_ddpm_steps = ctx.stage1_ddpm_steps + stage2_k_max = ctx.k_max if batch_size_auto: - est_hidden_dim, est_n_blocks = _batch_size_estimate_dims(model_cfg, training=False) + est_hidden_dim, est_n_blocks = _batch_size_estimate_dims(ctx.model_config, training=False) try: batch_size_value = gconfig.estimate_batch_size( est_hidden_dim, @@ -1037,42 +942,6 @@ def predict( assert batch_size_value is not None bs = batch_size_value - particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) - if particle_conditioning == "onehot" and pdg_topn_map is None: - typer.echo( - "error: checkpoint's conditioning.particle.type='onehot' but has " - "no pdg_topn_map — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - if material_conditioning == "onehot" and mat_topn_map is None: - typer.echo( - "error: checkpoint's conditioning.material.type='onehot' but has " - "no mat_topn_map — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} - mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} - cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) - tgt_norm = Normalizer.from_dict(ckpt["normalizer"]["target"]) - sec_phys_norm = Normalizer.from_dict(ckpt["normalizer"]["sec_phys"]) - - built = build_models(model_cfg) - model, sec_decoder = built["stage1"], built["stage2"] - if model is None or sec_decoder is None: - typer.echo( - "error: checkpoint has an inactive stage1 or stage2 — giant " - "predict needs both (see stage{1,2}_model.active)", - err=True, - ) - raise typer.Exit(1) - _load_model_weights(model, sec_decoder, ckpt, weights, checkpoint) - model.to(_device).eval() - sec_decoder.to(_device).eval() - - typer.echo(f"loaded checkpoint: {checkpoint} (weights: {weights.value})") - gconfig.warn_if_checkpoint_config_mismatch(checkpoint) # --- Output path --- out, dataset_path, pred_uuid = _resolve_prediction_output(data, out) @@ -1094,8 +963,12 @@ def predict( return iter_file_chunks(path, offset=offset, k_max=stage2_k_max) return iter_cond_chunks(path, offset=offset) - cond_pdg_topn = pdg_topn_map.class_map if particle_conditioning == "onehot" else None - cond_mat_topn = mat_topn_map.class_map if material_conditioning == "onehot" else None + # load_for_inference already guarantees pdg_topn_map/mat_topn_map are not + # None whenever the matching conditioning axis is "onehot" — the extra + # `is not None` conjuncts below are redundant at runtime, just narrowing + # for the type checker. + cond_pdg_topn = pdg_topn_map.class_map if pdg_topn_map is not None and particle_conditioning == "onehot" else None + cond_mat_topn = mat_topn_map.class_map if mat_topn_map is not None and material_conditioning == "onehot" else None def _concat(a: dict[str, np.ndarray], b: dict[str, np.ndarray]) -> dict[str, np.ndarray]: return {k: np.concatenate([a[k], b[k]], axis=0) for k in a} @@ -1405,65 +1278,24 @@ def rollout( _device = torch.device(device) if device else gconfig.auto_device() typer.echo(f"device: {_device}") - ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) - for key in ("model_config", "sec_decoder"): - if key not in ckpt: - typer.echo( - f"error: checkpoint has no {key} — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - - if "sec_phys" not in ckpt.get("normalizer", {}): - typer.echo( - "error: checkpoint has no normalizer.sec_phys — retrain with the current code", - err=True, - ) + try: + ctx = load_for_inference(checkpoint, _device, "rollout", weights=weights.value) + except CheckpointCompatibilityError as exc: + typer.echo(f"error: {exc}", err=True) raise typer.Exit(1) + typer.echo(f"loaded checkpoint: {checkpoint} (weights: {weights.value})") - gconfig.warn_if_checkpoint_config_mismatch(checkpoint) training_cfg = gconfig.load_checkpoint_config(checkpoint) - model_cfg = ckpt["model_config"] - particle_conditioning, material_conditioning = _conditioning_axes(model_cfg) - pdg_topn_map = _load_pdg_topn_map(ckpt) - mat_topn_map = _load_mat_topn_map(ckpt) - if particle_conditioning == "onehot" and pdg_topn_map is None: - typer.echo( - "error: checkpoint's conditioning.particle.type='onehot' but has " - "no pdg_topn_map — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - if material_conditioning == "onehot" and mat_topn_map is None: - typer.echo( - "error: checkpoint's conditioning.material.type='onehot' but has " - "no mat_topn_map — retrain with the current code", - err=True, - ) - raise typer.Exit(1) - other_policy = _particle_type_other_policy(model_cfg) - stage1_ddpm_steps = _ddpm_steps(model_cfg, "stage1") - stage2_ddpm_steps = _ddpm_steps(model_cfg, "stage2") - pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} - mat_map = {str(k): v for k, v in ckpt["mat_map"].items()} - cond_norm = Normalizer.from_dict(ckpt["normalizer"]["cond"]) - tgt_norm = Normalizer.from_dict(ckpt["normalizer"]["target"]) - sec_phys_norm = Normalizer.from_dict(ckpt["normalizer"]["sec_phys"]) - - built = build_models(model_cfg) - model, sec_decoder = built["stage1"], built["stage2"] - if model is None or sec_decoder is None: - typer.echo( - "error: checkpoint has an inactive stage1 or stage2 — giant " - "rollout needs both (see stage{1,2}_model.active)", - err=True, - ) - raise typer.Exit(1) - _load_model_weights(model, sec_decoder, ckpt, weights, checkpoint) - model.to(_device).eval() - sec_decoder.to(_device).eval() - typer.echo(f"loaded checkpoint: {checkpoint} (weights: {weights.value})") + assert ctx.stage1 is not None and ctx.stage2 is not None # require_stage2=True (default) guarantees this + model, sec_decoder = ctx.stage1, ctx.stage2 + cond_norm, tgt_norm, sec_phys_norm = ctx.cond_norm, ctx.tgt_norm, ctx.sec_phys_norm + pdg_map, mat_map = ctx.pdg_map, ctx.mat_map + pdg_topn_map, mat_topn_map = ctx.pdg_topn_map, ctx.mat_topn_map + particle_conditioning, material_conditioning = ctx.particle_conditioning, ctx.material_conditioning + other_policy = ctx.other_policy + stage1_ddpm_steps, stage2_ddpm_steps = ctx.stage1_ddpm_steps, ctx.stage2_ddpm_steps + model_cfg = ctx.model_config oracle = GeometryOracle.load(geometry) typer.echo(f"loaded geometry oracle: {geometry} (escape_threshold={oracle.escape_threshold:.3f})") @@ -1560,8 +1392,8 @@ def rollout( # model knob (router type/n_experts, noise_dim, vocab sizes, ...) # is available downstream without touching this command again. "model_config": dict(model_cfg), - "training_epoch": ckpt.get("epoch"), - "best_val_loss": ckpt.get("best_val_loss"), + "training_epoch": ctx.epoch, + "best_val_loss": ctx.best_val_loss, # [train]/[meta] from the sibling config.toml (giant.config.save_config) # — empty dicts if the checkpoint has no config.toml next to it. "training_config": dict(training_cfg.get("train", {})), diff --git a/tests/test_checkpoint_io.py b/tests/test_checkpoint_io.py new file mode 100644 index 0000000..8107945 --- /dev/null +++ b/tests/test_checkpoint_io.py @@ -0,0 +1,236 @@ +"""Tests for giant.checkpoint_io.load_for_inference (issues.md Issue 5) — +the shared bootstrap `giant predict`/`giant rollout` use to go from a +checkpoint path to ready-to-run models.""" + +from __future__ import annotations + +import copy + +import numpy as np +import pytest +import torch + +from giant import config as gconfig +from giant.checkpoint_io import ( + CheckpointCompatibilityError, + InferenceContext, + conditioning_axes, + load_for_inference, + stage_cfg, +) +from giant.data.loader import TopNMap +from giant.data.setup_cache import topnmap_to_json +from giant.data.transforms import Normalizer +from giant.model.network import build_models + +PDG_MAP = {11: 0, 22: 1, -11: 2} +MAT_MAP = {"G4_PbWO4": 0, "G4_AIR": 1} + + +def _model_cfg(stage2_active: bool = True) -> dict: + """DEFAULT_CONFIG-derived, shrunk for speed — same pattern as + tests/test_network.py::_minimal_model_config. Default `conditioning` + (both axes "physical") needs no top-N vocab map, so this is a cheap, + fully self-contained happy-path config.""" + cfg = copy.deepcopy(gconfig.DEFAULT_CONFIG) + cfg["conditioning"]["particle"]["emb_dim"] = 4 + cfg["conditioning"]["material"]["emb_dim"] = 4 + cfg["stage1_model"].update({"hidden_dim": 8, "n_res_blocks": 1}) + cfg["stage2_model"].update({"hidden_dim": 8, "n_res_blocks": 1, "k_max": 3}) + cfg["stage2_model"]["active"] = stage2_active + return { + "pdg_vocab": len(PDG_MAP), + "mat_vocab": len(MAT_MAP), + "conditioning": cfg["conditioning"], + "stage1_model": cfg["stage1_model"], + "stage2_model": cfg["stage2_model"], + } + + +def _norms() -> tuple[Normalizer, Normalizer, Normalizer]: + rng = np.random.default_rng(0) + cond = Normalizer().fit(rng.standard_normal((100, 15)).astype(np.float32)) + tgt = Normalizer().fit(rng.standard_normal((100, 9)).astype(np.float32)) + sec_phys = Normalizer().fit(rng.standard_normal((100, 2)).astype(np.float32)) + return cond, tgt, sec_phys + + +def _write_checkpoint(tmp_path, model_cfg=None, ema: bool = False, **ckpt_overrides): + cfg = model_cfg if model_cfg is not None else _model_cfg() + built = build_models(cfg) + stage1, stage2 = built["stage1"], built["stage2"] + cond, tgt, sec_phys = _norms() + ckpt: dict = { + "model_config": cfg, + "model": stage1.state_dict() if stage1 is not None else {}, + "sec_decoder": stage2.state_dict() if stage2 is not None else {}, + "pdg_map": PDG_MAP, + "mat_map": MAT_MAP, + "normalizer": {"cond": cond.to_dict(), "target": tgt.to_dict(), "sec_phys": sec_phys.to_dict()}, + "epoch": 3, + "best_val_loss": 0.5, + } + if ema: + ckpt["model_ema"] = stage1.state_dict() if stage1 is not None else {} + ckpt["sec_decoder_ema"] = stage2.state_dict() if stage2 is not None else {} + ckpt.update(ckpt_overrides) + path = tmp_path / "ckpt.pt" + torch.save(ckpt, path) + return path + + +def _onehot_model_cfg() -> dict: + cfg = _model_cfg() + cfg["conditioning"]["particle"]["type"] = "onehot" + return cfg + + +# --------------------------------------------------------------------------- +# Happy path +# --------------------------------------------------------------------------- + + +def test_happy_path_returns_populated_context(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + ctx = load_for_inference(checkpoint, torch.device("cpu"), "predict") + + assert isinstance(ctx, InferenceContext) + assert ctx.stage1 is not None and ctx.stage2 is not None + assert not ctx.stage1.training + assert not ctx.stage2.training + assert next(ctx.stage1.parameters()).device == torch.device("cpu") + assert ctx.pdg_map == PDG_MAP + assert ctx.mat_map == MAT_MAP + assert all(isinstance(k, int) for k in ctx.pdg_map) + assert all(isinstance(k, str) for k in ctx.mat_map) + assert ctx.particle_conditioning == "physical" + assert ctx.material_conditioning == "physical" + assert ctx.k_max == 3 + assert ctx.epoch == 3 + assert ctx.best_val_loss == 0.5 + assert ctx.model_config["stage1_model"]["hidden_dim"] == 8 + + +def test_happy_path_normalizer_values_round_trip(tmp_path): + cond, tgt, sec_phys = _norms() + checkpoint = _write_checkpoint(tmp_path) + ctx = load_for_inference(checkpoint, torch.device("cpu"), "predict") + + assert ctx.cond_norm.mean is not None and cond.mean is not None + assert ctx.tgt_norm.mean is not None and tgt.mean is not None + assert ctx.sec_phys_norm.mean is not None and sec_phys.mean is not None + np.testing.assert_allclose(ctx.cond_norm.mean, cond.mean) + np.testing.assert_allclose(ctx.tgt_norm.mean, tgt.mean) + np.testing.assert_allclose(ctx.sec_phys_norm.mean, sec_phys.mean) + + +# --------------------------------------------------------------------------- +# Guards +# --------------------------------------------------------------------------- + + +def test_missing_model_config_raises(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + ckpt = torch.load(checkpoint, weights_only=False) + del ckpt["model_config"] + torch.save(ckpt, checkpoint) + + with pytest.raises(CheckpointCompatibilityError, match="no model_config"): + load_for_inference(checkpoint, torch.device("cpu"), "predict") + + +def test_missing_sec_decoder_raises(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + ckpt = torch.load(checkpoint, weights_only=False) + del ckpt["sec_decoder"] + torch.save(ckpt, checkpoint) + + with pytest.raises(CheckpointCompatibilityError, match="no sec_decoder"): + load_for_inference(checkpoint, torch.device("cpu"), "predict") + + +def test_missing_sec_phys_normalizer_raises(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + ckpt = torch.load(checkpoint, weights_only=False) + del ckpt["normalizer"]["sec_phys"] + torch.save(ckpt, checkpoint) + + with pytest.raises(CheckpointCompatibilityError, match="no normalizer.sec_phys"): + load_for_inference(checkpoint, torch.device("cpu"), "predict") + + +def test_onehot_particle_conditioning_without_topn_map_raises(tmp_path): + checkpoint = _write_checkpoint(tmp_path, model_cfg=_onehot_model_cfg()) + + with pytest.raises(CheckpointCompatibilityError, match="pdg_topn_map"): + load_for_inference(checkpoint, torch.device("cpu"), "predict") + + +def test_onehot_particle_conditioning_with_topn_map_succeeds(tmp_path): + topn = TopNMap(class_map={11: 0, 22: 1}, other_members={}) + checkpoint = _write_checkpoint( + tmp_path, + model_cfg=_onehot_model_cfg(), + pdg_topn_map=topnmap_to_json(topn), + ) + ctx = load_for_inference(checkpoint, torch.device("cpu"), "predict") + assert ctx.particle_conditioning == "onehot" + assert ctx.pdg_topn_map is not None + assert ctx.pdg_topn_map.class_map == {11: 0, 22: 1} + + +def test_ema_weights_requested_but_missing_raises(tmp_path): + checkpoint = _write_checkpoint(tmp_path, ema=False) + + with pytest.raises(CheckpointCompatibilityError, match="no EMA weights"): + load_for_inference(checkpoint, torch.device("cpu"), "predict", weights="ema") + + +def test_ema_weights_requested_and_present_succeeds(tmp_path): + checkpoint = _write_checkpoint(tmp_path, ema=True) + ctx = load_for_inference(checkpoint, torch.device("cpu"), "predict", weights="ema") + assert ctx.stage1 is not None and ctx.stage2 is not None + + +@pytest.mark.parametrize("command_name", ["predict", "rollout"]) +def test_inactive_stage_with_require_stage2_raises_with_command_name(tmp_path, command_name): + checkpoint = _write_checkpoint(tmp_path, model_cfg=_model_cfg(stage2_active=False)) + + with pytest.raises(CheckpointCompatibilityError, match=f"{command_name} needs both"): + load_for_inference(checkpoint, torch.device("cpu"), command_name) + + +def test_inactive_stage_with_require_stage2_false_succeeds_with_stage2_none(tmp_path): + checkpoint = _write_checkpoint(tmp_path, model_cfg=_model_cfg(stage2_active=False)) + + ctx = load_for_inference(checkpoint, torch.device("cpu"), "predict", require_stage2=False) + assert ctx.stage1 is not None + assert ctx.stage2 is None + + +# --------------------------------------------------------------------------- +# conditioning_axes / stage_cfg +# --------------------------------------------------------------------------- + + +def test_conditioning_axes_v02_flat_string_applies_to_both_axes(): + assert conditioning_axes({"conditioning": "embedding"}) == ("embedding", "embedding") + + +def test_conditioning_axes_v03_nested_dict_independent_per_axis(): + model_cfg = {"conditioning": {"particle": {"type": "onehot"}, "material": {"type": "physical"}}} + assert conditioning_axes(model_cfg) == ("onehot", "physical") + + +def test_conditioning_axes_missing_key_uses_default(): + assert conditioning_axes({}, default="embedding") == ("embedding", "embedding") + + +def test_stage_cfg_new_shape_returns_subdict(): + model_cfg = {"stage2_model": {"k_max": 7}} + assert stage_cfg(model_cfg, "stage2") == {"k_max": 7} + + +def test_stage_cfg_v02_flat_shape_returns_empty_dict(): + model_cfg = {"hidden_dim": 32, "n_blocks": 4} + assert stage_cfg(model_cfg, "stage2") == {} diff --git a/tests/test_cli_predict.py b/tests/test_cli_predict.py index 1c18ae8..7c8e237 100644 --- a/tests/test_cli_predict.py +++ b/tests/test_cli_predict.py @@ -1,13 +1,18 @@ import uuid +import torch import yaml +from typer.testing import CliRunner from giant.cli import ( _CEPH_PREDICTIONS, _resolve_prediction_output, _write_prediction_ref, + app, ) +runner = CliRunner() + # --------------------------------------------------------------------------- # _resolve_prediction_output @@ -150,3 +155,20 @@ def test_ref_checkpoint_path_is_absolute(tmp_path): data = yaml.safe_load(ref_path.read_text()) assert data["checkpoint"].startswith("/") + + +# --------------------------------------------------------------------------- +# Bootstrap failure surfaces via the CLI (issues.md Issue 5 — confirms +# CheckpointCompatibilityError -> typer.Exit(1) actually wires up end-to-end, +# not just at the giant.checkpoint_io unit level). +# --------------------------------------------------------------------------- + + +def test_predict_exits_1_on_checkpoint_missing_model_config(tmp_path): + checkpoint = tmp_path / "bad.pt" + torch.save({"sec_decoder": {}, "normalizer": {"sec_phys": {}}}, checkpoint) + + result = runner.invoke(app, ["predict", "dummy.parquet", "--checkpoint", str(checkpoint)]) + + assert result.exit_code == 1 + assert "checkpoint has no model_config" in result.output diff --git a/tests/test_cli_rollout.py b/tests/test_cli_rollout.py new file mode 100644 index 0000000..b5bd2a6 --- /dev/null +++ b/tests/test_cli_rollout.py @@ -0,0 +1,33 @@ +"""Thin CLI smoke coverage for `giant rollout` (issues.md Issue 5) — confirms +the CheckpointCompatibilityError raised by giant.checkpoint_io.load_for_inference +surfaces as a clean typer.Exit(1) with the expected message, end-to-end +through the CLI, not just at the giant.checkpoint_io unit level.""" + +from __future__ import annotations + +import torch +from typer.testing import CliRunner + +from giant.cli import app + +runner = CliRunner() + + +def test_rollout_exits_1_on_checkpoint_missing_model_config(tmp_path): + checkpoint = tmp_path / "bad.pt" + torch.save({"sec_decoder": {}, "normalizer": {"sec_phys": {}}}, checkpoint) + + result = runner.invoke( + app, + [ + "rollout", + "dummy.parquet", + "--checkpoint", + str(checkpoint), + "--geometry", + "dummy_geometry.pkl", + ], + ) + + assert result.exit_code == 1 + assert "checkpoint has no model_config" in result.output -- 2.39.5 From 733c13c31c78cb1e6fbcdf463c893d58eba96179 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 12 Aug 2026 15:31:17 +0200 Subject: [PATCH 30/30] Mark issues.md Issue 5 as fixed Co-Authored-By: Claude Sonnet 5 --- issues.md | 35 ++++++++++++++++++++++++++++++++++- 1 file changed, 34 insertions(+), 1 deletion(-) diff --git a/issues.md b/issues.md index 7684edf..7d5a085 100644 --- a/issues.md +++ b/issues.md @@ -44,7 +44,7 @@ architecture matrix grows, not about rot or breakage. | 2 | No unknown-key validation — a typo in `config.toml` silently trains the wrong model | **High** | Small | **Fixed** | | 3 | `cli.py:train()` is a 58-parameter, 510-line fat controller | **High** | Medium | **Fixed** (`2bfb1ab`) | | 4 | `cli.py` is at 35.8 % coverage and holds untested override-precedence logic | **High** | Medium | **Fixed** (`2bfb1ab`, partial — see status note) | -| 5 | Inference bootstrap is duplicated verbatim between `predict` and `rollout` | **High** | Small | Open | +| 5 | Inference bootstrap is duplicated verbatim between `predict` and `rollout` | **High** | Small | **Fixed** | | 6 | Two independent v0.2→v0.3 migration surfaces encode the same knowledge | Medium | Medium | Open | | 7 | Positional tuple contracts (9-tuple, 7-tuple) between data, model and training layers | Medium | Small | Open | | 8 | `network.py` is 1745 lines holding three distinct modules | Medium | Small | Open | @@ -622,6 +622,39 @@ extraction. ## Issue 5 — The inference bootstrap is duplicated verbatim between `predict` and `rollout` +> **Status: Fixed.** `giant/checkpoint_io.py` now holds a single +> `load_for_inference(checkpoint, device, command_name, weights="raw", require_stage2=True)` +> plus an `InferenceContext` dataclass (stage1/stage2 models, all three normalizers, both +> vocab maps, both top-N maps, both conditioning axes, `k_max`, both stages' ddpm step +> counts, `other_policy`, the raw `model_config`, and `epoch`/`best_val_loss` for +> `rollout`'s YAML sidecar) — exactly the design this issue proposed, verified against the +> current (not `55332db`-era) code before writing it. `predict`/`rollout` in `cli.py` +> each shrink to one `try/except CheckpointCompatibilityError` call plus a block of +> `ctx.` unpacks; `cli.py` lost `_conditioning_axes`, `_stage_cfg`, `_ddpm_steps`, +> `_particle_type_other_policy`, `_load_pdg_topn_map`/`_load_mat_topn_map`, and +> `_load_model_weights` entirely (net ~180 lines off `cli.py`). The independent third copy +> in `giant/analysis/router_gating.py` was deleted in favour of a lazy +> `from giant.checkpoint_io import conditioning_axes` inside `load_router`'s existing +> lazy-import block, preserving that module's "polars/numpy only at module scope" +> contract (`checkpoint_io.py` imports torch eagerly, so it must never be imported at +> `router_gating.py` module scope). Two guard orderings from the two commands' drifted +> copies were consolidated into one (`warn_if_checkpoint_config_mismatch` now always +> runs right after the existence guards, and `predict`'s `--batch-size auto` estimate now +> reads `ctx.model_config` after the checkpoint loads rather than before) — both are +> console-output-order changes only, no error text or model behavior changed, confirmed by +> diffing `giant predict --help`/`giant rollout --help` byte-for-byte before and after (no +> flag touched) and re-reading both rewritten command bodies field-by-field against the +> original. `require_stage2` exists as a real parameter, exercised by a new test, even +> though both current callers pass the default `True`. New `tests/test_checkpoint_io.py` +> (17 tests: happy path, every guard individually with exact message-text assertions, the +> `require_stage2=False`/inactive-stage2 path, `conditioning_axes`/`stage_cfg` directly) +> plus thin `CliRunner` smoke tests in `tests/test_cli_predict.py` and the new +> `tests/test_cli_rollout.py` confirming `CheckpointCompatibilityError` actually surfaces +> as `typer.Exit(1)` through the CLI — previously this entire code path had zero test +> coverage. `uv run pytest -q` (803 passed, up from 784), `ruff check`, `ruff format +> --check`, and `ty check` all clean. Everything below this point describes the pre-fix +> state and is kept for historical context. + **Severity: High. Effort: Small. Risk if unfixed: silent train/inference skew.** **Location:** `giant/cli.py:1121-1201` (`predict`), `giant/cli.py:1534-1593` (`rollout`), -- 2.39.5