feat: add WGAN + AR stop-token config variant

Branches off configs/baseline.toml with both stages on WGAN-GP and
stage-2 n_sec.mode = stop_token, 30 epochs — combines two unbenchmarked
roadmap axes (post-v0.3.0 WGAN, and the AR stop-token multiplicity mode)
into one variant that stays a single edit away from baseline for
attribution.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KzPghrmFcAJYrWUvHApY9N

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.3.15"
+current_version = "0.3.16"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 search = "{current_version}"

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
+## [0.3.16] - 2026-08-31
+
+### Changed
+
+- Docs: record analysis_341dfb14 baseline rollout benchmark results
+
+- Feat: add eval-cost benchmark — Geant4 reference vs surrogate rollout timing
+
 ## [0.3.15] - 2026-08-28
 
 ### Changed

CLAUDE.md

@@ -113,6 +113,8 @@ Secondary energies are a **stick-breaking partition of the `e_sec` budget** from
 
 **v0.3.0 — Stage-2 autoregressive redesign (implemented, released; on `master` since 2026-08-13):** motivated by the 2026-08-03 WGAN rollout benchmark, which failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). Stage 2 became autoregressive in descending-energy order with teacher forcing, and the particle-type representation went back to **categorical** (`particle_type.target = "onehot"`), reversing the 2026-07-17 continuous `(log-mass, charge)` target. The config break (`[conditioning]`/`[stage1_model]`/`[stage2_model]`/`[train]` replacing the flat `train.mode` + `[model]`) makes per-stage generators, stage-2-only training, and one-shot-vs-autoregressive comparison all expressible, and the `network.py` refactor into composable parts (encoder × trunk × objective) also makes routed WGAN work for the first time.
 
+**Baseline benchmark (done, 2026-08-26):** `configs/baseline.toml`'s first full rollout-vs-Geant4 validation (`analysis_341dfb14`, checkpoint `20260814_1743_s2-flow_h512_s2h512_bs36864_ep50/best.pt`, epoch 50/50). Confirms the v0.3.0 pivot fixed the species collapse — zero photon secondaries / hallucinated `-14` muon antineutrinos are both gone (γ at 95% of truth, no `-14` in the top species) — and rules out `conditioning.*.type = "physical"` as the cause, since this checkpoint pairs it with `flow`/no-router and still doesn't collapse. Bulk shower observables are close to Geant4 (total deposited energy +1.9%, containment depth-90%/95% both 0.986×), but steps/event now *over*-shoots by 1.32× (the opposite sign from every pre-v0.3.0 checkpoint), no hadronic/nuclear secondaries are produced at all, and event-to-event energy variance is ~16× too narrow. Writeup: `/home/lars/knowledge-base/experiments/giant-baseline-flow-ar-rollout-validation.md`.
+
 v0.2 configs and checkpoints are auto-migrated (`config.migrate_config`, `model._legacy._migrate_legacy_model_config`, both drawing on shared facts in `giant/_migration.py`). **v0.2 checkpoint-loading support has no expiry decided yet**: `/ceph` still holds pre-v0.3.0 checkpoints and analysis runs referencing them, so don't delete or substantially alter either migration function or `tests/legacy/network_v02_snapshot.py` (the frozen v0.2 snapshot they're tested against) without an explicit decision to do so first.
 
 **Faster-eval architectures — both implemented, neither validated.** Target is a ~10× native-Geant4 eval budget; no eval-latency number exists for any configuration yet, so that budget is unverified across the board.

configs/baseline.toml

@@ -29,11 +29,21 @@
 #     capacity overfitting is not the binding constraint, and every recent
 #     run used 0.0.
 #
-# Known weak spots this baseline is expected to *exhibit* (they are the
+# Known weak spots, now measured against this exact config rather than
-# reason for the comparisons, not a reason to retune this file): every model
+# extrapolated from the pre-v0.3 field (analysis_341dfb14, best.pt @ epoch
-# on record under-produces steps per event by ~2x (rollout ~7e4 vs Geant4
+# 50/50, full writeup: knowledge-base/experiments/
-# ~1.4e5) and secondaries per event by 2-3.5x (~2-3e4 vs 7.2e4), and n_sec
+# giant-baseline-flow-ar-rollout-validation.md). Unlike every pre-v0.3
-# head accuracy sits at 0.863-0.867 regardless of size or objective.
+# checkpoint (which under-produced steps/event by 1.6-5x), this baseline
+# OVER-produces steps/event by 1.32x (1.86e5 vs Geant4 1.41e5) and
+# under-produces secondaries/event by 0.84x (5.97e4 vs 7.14e4) — the sign on
+# steps flipped with the v0.3 autoregressive pivot, so don't assume it still
+# undershoots. Secondary-species hallucination (zero photons, hallucinated
+# `-14` muon antineutrinos) that broke every prior checkpoint is gone; the
+# remaining species gap is a total absence of hadronic/nuclear secondaries
+# (protons, neutrons, ion recoils), not miscalibration of the ones produced.
+# Total deposited energy/event is +1.9% high but its event-to-event spread is
+# ~16x too narrow (31 MeV vs Geant4's 491 MeV). Per-step deposited energy is
+# the worst per-step marginal (KS 0.179 vs 0.004-0.071 for the others).
 
 [meta]
 # REQUIRED. Without it config.migrate_config reads this file as v0.2 and

configs/wgan_ar_stoptoken.toml

@@ -0,0 +1,122 @@
+# GIANT WGAN-GP + AR stop-token variant of configs/baseline.toml.
+#
+# Two roadmap axes, combined into one run: WGAN-GP generators for both
+# stages (unbenchmarked since the 2026-08-03 pre-v0.3.0 failure, which was
+# secondary-species mode collapse — the failure v0.3.0's AR/categorical
+# pivot exists to fix) and the AR stop-token multiplicity mode
+# (stage2_model.n_sec.mode = "stop_token", never benchmarked at all).
+# Everything else is byte-identical to baseline.toml so a rollout compared
+# against baseline's analysis_341dfb14 is attributable to these two axes
+# alone: conditioning (physical/physical), hidden_dim 512 / n_res_blocks 6 /
+# dropout 0.0 per stage, k_max 15, history "markov", teacher_forcing
+# "always", particle_type.target "onehot" (n_classes 32, other_policy
+# "sample"), lr 3e-4, warmup_epochs 3, weight_decay 0.01, ema_decay 0.9999,
+# val_fraction 0.1, num_workers 4, seed 0, validate_steps 10, W&B on.
+#
+# No [stage1_model.wgan] / [stage2_model.wgan] block: the dataclass defaults
+# (noise_dim 64, n_critic 5, gp_weight 10.0, critic_lr 0.0 = inherit
+# train.lr, critic_hidden_dim/critic_n_res_blocks 0 = inherit the stage's
+# 512/6, stage 2's gumbel_tau_start/_end 1.0/0.1) are what the earlier WGAN
+# runs used — writing them out would add keys that don't vary.
+#
+# particle_type.class_weighting stays "none" (the default): config.py's
+# validate_config rejects any other value under stage2_model.generator =
+# "wgan", since that path feeds the type slice to the critic via a
+# straight-through Gumbel relaxation instead of a weighted cross-entropy.
+#
+# Prior WGAN writeup (pre-v0.3.0, describes the failure this run re-tests):
+# /home/lars/knowledge-base/experiments/giant-wgan-physical-rollout-validation.md
+
+[meta]
+# REQUIRED. Without it config.migrate_config reads this file as v0.2 and
+# rewrites it from V02_FIXED_FACTS — silently forcing decoder = "one_shot",
+# particle_type.target = "physical" and the v0.2 default sizes, while still
+# passing validate_config.
+config_version = 3
+
+[conditioning]
+# Physical-property MLPs rather than learned vocab embeddings: computable for
+# any PDG code / material, which is what the held-out-species and
+# held-out-material generalization comparisons need.
+out_dim      = 128
+share_stages = false
+
+# n_layers = 2 rather than the v0.3 default of 1: v0.2's conditioning MLP was
+# always 2 deep (see _migration.V02_FIXED_FACTS), so this keeps the encoder
+# identical to baseline.toml.
+[conditioning.particle]
+type     = "physical"
+emb_dim  = 16
+n_layers = 2
+
+[conditioning.material]
+type     = "physical"
+emb_dim  = 16
+n_layers = 2
+
+[stage1_model]
+generator    = "wgan"
+hidden_dim   = 512
+n_res_blocks = 6
+dropout      = 0.0
+
+[stage2_model]
+# Autoregressive in descending-energy order, as baseline.toml — this variant
+# only swaps the generator (flow -> wgan) and the multiplicity mode
+# (head -> stop_token), not the decoder shape.
+decoder      = "autoregressive"
+generator    = "wgan"
+hidden_dim   = 512
+n_res_blocks = 6
+dropout      = 0.0
+k_max        = 15
+
+[stage2_model.autoregressive]
+history         = "markov"
+teacher_forcing = "always"
+
+[stage2_model.n_sec]
+# EOS-style per-slot stop head on the AR secondary decoder, replacing the
+# n_sec classifier entirely (mutually exclusive — see NSecConfig's
+# docstring in giant/config.py). Requires decoder = "autoregressive" and
+# owner = "stage2" (both already true above/by default); validate_config
+# enforces this.
+mode = "stop_token"
+
+[stage2_model.particle_type]
+target = "onehot"
+# Decoupled from conditioning.particle.emb_dim (gitea #29). 32 classes + the
+# "other" bucket keeps essentially all real secondary species out of "other"
+# without making the head expensive.
+n_classes    = 32
+other_policy = "sample"
+
+[train]
+epochs = 30
+# Halved from baseline's 36864. That figure came from a measured linear fit
+# of the *flow-AR* training step (peak reserved MiB = 0.9736 * batch_size +
+# 115); WGAN invalidates it twice over — each stage gains a critic that by
+# default inherits the stage's own 512/6 body, and gradient_penalty
+# (giant/model/wgan.py, forced fp32 internally) runs a double-backward every
+# batch. 18432 is a conservative choice pending a real memory measurement on
+# this exact config, not a re-derived fit. Throughput is already flat above
+# bs~4096 on the 4070, so this costs occupancy on the L40S, not step
+# efficiency.
+batch_size    = 18432
+lr            = 3e-4
+warmup_epochs = 3
+weight_decay  = 0.01
+ema_decay     = 0.9999
+val_fraction  = 0.1
+num_workers   = 4
+seed          = 0
+# Tightened from baseline's 10: WGANStageTrainer.supports_val_loss = False,
+# and with both stages adversarial there is no per-epoch val loss at all, so
+# validate_every's marginal-KL pass (giant/training/trainers.py's
+# val_objective) is the only comparable-across-epochs best-checkpoint
+# selection signal available. 5 gives 6 evaluations over 30 epochs instead
+# of baseline's 3, at ~6x5000s of extra walltime.
+validate_every = 5
+validate_steps = 10
+wandb          = true
+wandb_project  = "giant"

giant/analysis/catalog.py

@@ -45,6 +45,7 @@ import numpy as np
 import polars as pl
 
 from giant.analysis.context import Context
+from giant.analysis.geant4_reference import GEANT4_REFERENCE, geant4_per_step_us
 from giant.analysis.grouping import (
     energy_bin_labels,
     event_energy_bins,
@@ -125,6 +126,7 @@ class Bundle:
                 phys=physical_steps(r_all, Side.rollout),
                 checkpoint=rs.checkpoint,
                 type_embedding_l1_dist=rs.type_embedding_l1_dist,
+                timing=rs.timing,
             )
         return cls(ctx=ctx, rollouts=sides, t_all=t_all, t_phys=physical_steps(t_all, Side.reference))
 
@@ -973,6 +975,80 @@ def _sec_cos_angle_finalize(parts: list[dict], ctx: Context) -> Reduced:
     )
 
 
+# ---------------------------------------------------------------------------
+# eval cost (not chunked — metadata-only, no row scan)
+# ---------------------------------------------------------------------------
+
+_EVAL_COST_LABELS = ["sampling / simulation", "parquet write / convert", "total"]
+_EVAL_COST_NOTE = (
+    "no rollout in this run carries a `timing` block — re-run `giant rollout` "
+    "(timing instrumentation added after this checkpoint's rollout run) to "
+    "populate this plot"
+)
+
+
+def _eval_cost_per_step(b: Bundle) -> Reduced:
+    """Per-rollout µs/physical-step vs the measured Geant4 reference.
+
+    ``timing`` (``giant.cli``'s ``rollout`` command) is metadata carried on
+    the rollout YAML, not derived from the row data, so this needs no chunked
+    scan — same shape as the router diagnostics above.
+    """
+    series: dict[str, list[float]] = {}
+    speedup: dict[str, float] = {}
+    for name, rs in b.rollouts.items():
+        t = rs.timing
+        if not t or t.get("us_per_step") is None:
+            continue
+        sample_us = t["us_per_step"]
+        write_us = t.get("write_us_per_step") or 0.0
+        series[name] = [sample_us, write_us, sample_us + write_us]
+
+    if not series:
+        return Reduced(
+            id="eval_cost_per_step",
+            family="cost",
+            kind="unavailable",
+            title="Eval cost per step: surrogate vs Geant4",
+            xlabel="n/a",
+            payload={"note": _EVAL_COST_NOTE},
+        )
+
+    g4 = geant4_per_step_us()
+    reference = [g4["sim_us_per_step"], g4["convert_us_per_step"], g4["total_us_per_step"]]
+    for name, vals in series.items():
+        speedup[name] = reference[-1] / vals[-1] if vals[-1] else float("inf")
+
+    return Reduced(
+        id="eval_cost_per_step",
+        family="cost",
+        kind="bar",
+        title="Eval cost per step: surrogate vs Geant4",
+        xlabel="phase",
+        payload={
+            "labels": _EVAL_COST_LABELS,
+            "series": series,
+            "reference": reference,
+            "ylabel": "µs per physical step",
+            "log_y": True,
+        },
+        meta={
+            "speedup_vs_geant4_total": speedup,
+            "geant4_provenance": GEANT4_REFERENCE["provenance"],
+            "caveat": (
+                "The Geant4 reference is measured single-threaded on one CPU core "
+                "(see giant.analysis.geant4_reference); a rollout's timing is "
+                "whatever device it actually ran on (see each series' device in "
+                "run_meta.json's plot_meta). This is a deployment-speedup ratio, "
+                "not a same-hardware or per-FLOP comparison."
+            ),
+        },
+    )
+
+
+_eval_cost_per_step_partial, _eval_cost_per_step_finalize = _unchunkable(_eval_cost_per_step)
+
+
 # ---------------------------------------------------------------------------
 # router diagnostics (not chunked — already bounded/subsampled)
 # ---------------------------------------------------------------------------
@@ -1160,6 +1236,13 @@ def build_catalog() -> list[PlotSpec]:
             compute_partial=_sec_cos_angle_partial,
             finalize=_sec_cos_angle_finalize,
         ),
+        PlotSpec(
+            "eval_cost_per_step",
+            "cost",
+            compute_partial=_eval_cost_per_step_partial,
+            finalize=_eval_cost_per_step_finalize,
+            chunkable=False,
+        ),
         PlotSpec(
             "router_gating",
             "model",

giant/analysis/condor.py

@@ -82,6 +82,7 @@ _PLOT_META_KEYS = (
     "rollout_seed",
     "n_rows",
     "termination_reason_counts",
+    "timing",
     "model_config",
     "training_epoch",
     "best_val_loss",
@@ -329,9 +330,9 @@ def compute_reduced(
 ) -> Path:
     """Core: run one (plot, chunk)'s partial reduction against explicit paths.
 
-    ``rollouts``: ``[{"name", "path", "checkpoint"?, "type_embedding_l1_dist"?},
+    ``rollouts``: ``[{"name", "path", "checkpoint"?, "type_embedding_l1_dist"?,
-    ...]``, one per rollout series (insertion order preserved through to every
+    "timing"?}, ...]``, one per rollout series (insertion order preserved
-    plot's ``Reduced.payload["series"]``).
+    through to every plot's ``Reduced.payload["series"]``).
 
     Writes a ``Partial`` JSON — the raw, not-yet-merged output of
     ``PlotSpec.compute_partial`` — never a finished ``Reduced``; ``merge_one``
@@ -352,6 +353,7 @@ def compute_reduced(
             source=r["path"],
             checkpoint=r.get("checkpoint"),
             type_embedding_l1_dist=r.get("type_embedding_l1_dist"),
+            timing=r.get("timing"),
         )
         for r in rollouts
     ]
@@ -377,6 +379,7 @@ def compute_one(spec_id: str, run_dir: str | Path, chunk_index: int = 0) -> Path
             "path": ro["path"],
             "checkpoint": ro["plot_meta"].get("checkpoint"),
             "type_embedding_l1_dist": ro["plot_meta"].get("type_embedding_l1_dist"),
+            "timing": ro["plot_meta"].get("timing"),
         }
         for ro in meta.rollouts
     ]

giant/analysis/geant4_reference.py

@@ -0,0 +1,76 @@
+"""Measured Geant4 (miniCaloSim) per-step eval cost — the reference line for
+``eval_cost_per_step`` in ``catalog.py``.
+
+Mirrors the precedent set by ``runtime_estimate.py``'s ``_COST_MODEL``: a
+constant table measured once on a specific machine and pasted in, with the
+methodology and provenance recorded in this docstring rather than derived at
+runtime (there is no live Geant4 install on the machines that run
+``giant analyze``, and re-measuring per invocation would be both slow and
+noisy — see the module docstring precedent).
+
+**Methodology** (``scratchpad/bench_geant4.py``, a one-off, not a `dwarf`
+subcommand): ``run_pbwo4`` (the default homogeneous-PbWO4 miniCaloSim
+executable, see ``~/Programming/minicalosim``) was timed at 3 beam energies
+(1/10/50 GeV) and **4 event counts each**, converting each run's ROOT output
+to Parquet with ``giant.tools.steps_to_parquet.convert_steps_to_parquet``
+immediately after. Event counts were scaled down as energy rose (100/400/
+1000/2000 at 1 GeV, 30/100/200/300 at 10 GeV, 10/25/45/60 at 50 GeV) to keep
+every run's row count under ~8.1M — a naive 50/200 pair at 50 GeV produces
+~27M steps and OOM'd the conversion step on a 14GB laptop. Per-energy linear
+fits (``t = intercept + slope * n``) separate Geant4's one-time init (physics
+tables, geometry construction) from its true marginal per-event cost — the
+slope, not a naive ``t / n_events`` from a single run, is what feeds
+``sim_us_per_step`` below. The per-step denominator is the produced
+``Steps``-tree/Parquet row count, matching the "physical step" unit
+``giant rollout``'s ``timing.n_physical_rows`` uses on the surrogate side.
+Both stages ran single-threaded (default Geant4 threading), pinned to one
+CPU core.
+
+``sim_us_per_step``/``convert_us_per_step``/``sim_ms_per_event`` below are
+the mean across the 3 energies. With 4 event-count points per energy (up
+from an initial 2-point pass, which had ~80% spread and nonsensical negative
+fitted intercepts at 10/50 GeV — an artifact of extrapolating a 2-point
+line), both quantities are now energy-flat as physically expected:
+``sim_us_per_step`` spread ~5%, ``convert_us_per_step`` spread ~13.5%. Treat
+these as reliable to about that precision.
+
+**Caveat — hardware asymmetry**: this reference is single-core CPU. A
+surrogate rollout's ``timing`` block will typically be measured on a batched
+GPU. The resulting ratio in ``eval_cost_per_step`` is a *deployment* speedup
+(what you'd actually see swapping Geant4 for the surrogate in a production
+pipeline), not a same-hardware or per-FLOP comparison — state this whenever
+quoting the number.
+
+**Staleness**: re-run ``scratchpad/bench_geant4.py`` (and update this file)
+if measured on different hardware, after a miniCaloSim/Geant4 version bump,
+or if this reference is more than a year or two stale.
+"""
+
+from __future__ import annotations
+
+GEANT4_REFERENCE: dict = {
+    "sim_us_per_step": 11.2903,
+    "convert_us_per_step": 11.1014,
+    "sim_ms_per_event": 609.6848,
+    "provenance": {
+        "cpu": "AMD Ryzen 7 PRO 4750U with Radeon Graphics",
+        "geant4_version": "11.4.1",
+        "minicalosim_sha": "ea917da",
+        "measured": "2026-08-31",
+        "energies_gev": [1.0, 10.0, 50.0],
+        "spread_pct_sim": 4.96,
+        "spread_pct_convert": 13.52,
+        "threads": 1,
+    },
+}
+
+
+def geant4_per_step_us() -> dict[str, float]:
+    """Sim / convert / total microseconds per physical step, from ``GEANT4_REFERENCE``."""
+    sim = GEANT4_REFERENCE["sim_us_per_step"]
+    convert = GEANT4_REFERENCE["convert_us_per_step"]
+    return {
+        "sim_us_per_step": sim,
+        "convert_us_per_step": convert,
+        "total_us_per_step": sim + convert,
+    }

giant/analysis/render.py

@@ -274,6 +274,8 @@ def _render_bar(r: Reduced, params: dict):
     ax.set_xticks(x)
     ax.set_xticklabels(labels, rotation=45, ha="right")
     ax.set_ylabel(r.payload.get("ylabel", "value"))
+    if r.payload.get("log_y"):
+        ax.set_yscale("log")
     ps.style_legend(ax, title="source")
     return fig
 

giant/analysis/runtime_estimate.py

@@ -96,6 +96,10 @@ _COST_MODEL: dict[str, tuple[float, float]] = {
     "sec_count_per_species": (0.0, 4.963e-07),
     "sec_energy": (0.0, 4.727e-07),
     "sec_cos_angle": (0.0, 2.749e-06),
+    # Metadata-only (YAML-carried `timing`, no row scan) — same shape as the
+    # router diagnostics' fixed cost, just cheaper since there's no live
+    # torch checkpoint to load.
+    "eval_cost_per_step": (0.0, 0.0),
 }
 
 

giant/analysis/sources.py

@@ -110,6 +110,7 @@ class RolloutSpec:
     source: str | Path | pl.LazyFrame
     checkpoint: str | None = None
     type_embedding_l1_dist: dict | None = None
+    timing: dict | None = None
 
 
 @dataclass
@@ -124,6 +125,10 @@ class RolloutSide:
     # only. Unlike checkpoint, this needs no live model: it's already a
     # finished histogram, just passed through.
     type_embedding_l1_dist: dict | None = None
+    # Wall-clock cost of this rollout run (giant.cli's rollout command),
+    # from the rollout YAML — eval_cost_per_step only. None on rollout runs
+    # that predate timing instrumentation.
+    timing: dict | None = None
 
 
 def _check_rollout_metadata(path: Path) -> None:

giant/cli.py

@@ -6,7 +6,7 @@ from enum import Enum
 import math
 from pathlib import Path
 import re
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, cast
 import uuid as uuid_mod
 
 import typer
@@ -205,6 +205,45 @@ def _write_prediction_ref(
     return ref_path
 
 
+def _build_rollout_timing(
+    *,
+    setup_s: float,
+    rollout_s: float,
+    write_s: float,
+    n_rows: int,
+    termination_reason_counts: dict[str, int],
+    n_seed_events: int,
+    device: str,
+    torch_threads: int,
+) -> dict:
+    """Assemble ``giant rollout``'s ``timing`` sidecar block.
+
+    ``n_physical_rows`` excludes the synthetic termination rows (escape/
+    unknown-pdg/energy-cutoff/max-steps markers `giant.rollout` emits but
+    Geant4 never does) so ``us_per_step`` is comparable to
+    ``giant.analysis.geant4_reference``'s per-step Geant4 measurement — see
+    ``giant/analysis/catalog.py``'s ``eval_cost_per_step`` spec.
+    """
+    from giant.analysis.sources import SYNTHETIC_TERMINATION_REASONS
+
+    sample_s = rollout_s - write_s
+    n_synthetic_rows = sum(termination_reason_counts.get(reason, 0) for reason in SYNTHETIC_TERMINATION_REASONS)
+    n_physical_rows = n_rows - n_synthetic_rows
+    return {
+        "setup_s": setup_s,
+        "rollout_s": rollout_s,
+        "write_s": write_s,
+        "sample_s": sample_s,
+        "n_rows": n_rows,
+        "n_physical_rows": n_physical_rows,
+        "us_per_step": (sample_s / n_physical_rows * 1e6) if n_physical_rows else None,
+        "write_us_per_step": (write_s / n_physical_rows * 1e6) if n_physical_rows else None,
+        "ms_per_event": (rollout_s / n_seed_events * 1e3) if n_seed_events else None,
+        "device": device,
+        "torch_threads": torch_threads,
+    }
+
+
 @app.callback()
 def _main() -> None:
     """GIANT — Geant4 step-function surrogate."""
@@ -1455,6 +1494,8 @@ def rollout(
     ] = None,
 ) -> None:
     """Roll the surrogate forward into full showers (autoregressive)."""
+    import time
+
     import numpy as np
     import pyarrow as pa
     import pyarrow.parquet as pq
@@ -1464,7 +1505,9 @@ def rollout(
     from giant.checkpoint_io import CheckpointCompatibilityError, load_for_inference
     from giant.data.loader import find_parquet_files
     from giant.geometry import GeometryOracle
-    from giant.rollout import L1DistCollector, rollout as run_rollout
+    from giant.rollout import L1DistCollector, RolloutSummary, rollout as run_rollout
+
+    _t_setup_start = time.perf_counter()
 
     if seed is not None:
         torch.manual_seed(seed)
@@ -1511,9 +1554,11 @@ def rollout(
     # avg_tracks_per_event) — mirrors the row-group streaming `giant predict`
     # already does on its input side.
     writer: pq.ParquetWriter | None = None
+    _write_s = 0.0
 
     def _write_chunk(row: dict[str, np.ndarray]) -> None:
-        nonlocal writer
+        nonlocal writer, _write_s
+        _t0 = time.perf_counter()
         table = pa.table(row)
         if writer is None:
             table = table.replace_schema_metadata(
@@ -1524,11 +1569,14 @@ def rollout(
             )
             writer = pq.ParquetWriter(out, table.schema)
         writer.write_table(table)
+        _write_s += time.perf_counter() - _t0
 
     # Only meaningful under particle_type.target="embedding" — a
     # no-op collector otherwise, cheaper than branching the call itself.
     l1_dist_collector = L1DistCollector()
 
+    _setup_s = time.perf_counter() - _t_setup_start
+    _t_rollout_start = time.perf_counter()
     summary = run_rollout(
         model,
         sec_decoder,
@@ -1560,6 +1608,23 @@ def rollout(
     )
     if writer is not None:
         writer.close()
+    # on_chunk=_write_chunk is always passed above, so rollout() always
+    # returns the streaming-summary shape (RolloutSummary), never the
+    # materialized dict[str, np.ndarray] alternative its return type allows.
+    summary = cast(RolloutSummary, summary)
+    _rollout_s = time.perf_counter() - _t_rollout_start
+    timing = _build_rollout_timing(
+        setup_s=_setup_s,
+        rollout_s=_rollout_s,
+        write_s=_write_s,
+        n_rows=summary["n_rows"],
+        termination_reason_counts=summary["termination_reason_counts"],
+        n_seed_events=len(seeds["event_id"]),
+        device=str(_device),
+        torch_threads=torch.get_num_threads(),
+    )
+    _sample_s = timing["sample_s"]
+    n_physical_rows = timing["n_physical_rows"]
 
     l1_summary = l1_dist_collector.summary()
 
@@ -1582,6 +1647,10 @@ def rollout(
             "rollout_seed": seed,
             "n_rows": summary["n_rows"],
             "termination_reason_counts": summary["termination_reason_counts"],
+            # Wall-clock cost of this run, normalized per physical step (the
+            # comparable unit against giant.analysis.geant4_reference) — see
+            # eval_cost_per_step in giant/analysis/catalog.py.
+            "timing": timing,
             # Diagnostic — only present under
             # stage2_model.particle_type.target="embedding"; omitted (not
             # written as null) otherwise, so giant.analysis can tell "not
@@ -1605,6 +1674,11 @@ def rollout(
 
     typer.echo(f"wrote {summary['n_rows']:,} step rows → {out}")
     typer.echo(f"terminations: {summary['termination_reason_counts']}")
+    if timing["us_per_step"] is not None:
+        typer.echo(
+            f"timing: {_rollout_s:.1f}s total ({_sample_s:.1f}s sample + {_write_s:.1f}s write), "
+            f"{timing['us_per_step']:.1f} us/step over {n_physical_rows:,} physical steps"
+        )
     typer.echo(f"reference: {ref_path}")
 
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "giant"
-version = "0.3.15"
+version = "0.3.16"
 description = "Geant4 step-function surrogate via conditional flow matching"
 readme = "README.md"
 requires-python = ">=3.12"

tests/test_catalog.py

@@ -261,3 +261,32 @@ def test_sec_count_per_step_by_species_zero_row_is_per_species(bundle):
     for j, _ in enumerate(cols):
         if j != g:
             assert ref[0][j] == 3 and sum(row[j] for row in ref[1:]) == 0
+
+
+# ---------------------------------------------------------------------------
+# eval_cost_per_step
+# ---------------------------------------------------------------------------
+
+
+def test_eval_cost_per_step_unavailable_without_timing(bundle: Bundle):
+    # `bundle`'s RolloutSpec carries no `timing` -> no rollout to compare.
+    spec = get_spec("eval_cost_per_step")
+    r = spec.finalize([spec.compute_partial(bundle)], bundle.ctx)
+    assert r.kind == "unavailable"
+    assert r.payload["note"]
+
+
+def test_eval_cost_per_step_bar_with_timing(ctx: Context):
+    spec = get_spec("eval_cost_per_step")
+    rs = RolloutSpec(
+        "rollout",
+        _rollout_frame(),
+        timing={"us_per_step": 12.5, "write_us_per_step": 2.5},
+    )
+    b = Bundle.open([rs], _reference_frame(), ctx)
+    r = spec.finalize([spec.compute_partial(b)], ctx)
+    assert r.kind == "bar"
+    assert r.payload["series"]["rollout"] == [12.5, 2.5, 15.0]
+    assert len(r.payload["reference"]) == 3
+    assert r.payload["log_y"] is True
+    assert "rollout" in r.meta["speedup_vs_geant4_total"]

tests/test_cli_rollout.py

@@ -8,11 +8,52 @@ from __future__ import annotations
 import torch
 from typer.testing import CliRunner
 
-from giant.cli import app
+from giant.cli import _build_rollout_timing, app
 
 runner = CliRunner()
 
 
+def test_build_rollout_timing_excludes_synthetic_rows_from_per_step_cost():
+    # 100 rows total, 30 of them synthetic termination markers (escape) ->
+    # us_per_step should be normalized over the 70 physical rows only, the
+    # same unit giant.analysis.geant4_reference measures Geant4 in.
+    timing = _build_rollout_timing(
+        setup_s=1.0,
+        rollout_s=10.0,
+        write_s=2.0,
+        n_rows=100,
+        termination_reason_counts={"escaped": 30, "natural_end": 70},
+        n_seed_events=5,
+        device="cpu",
+        torch_threads=4,
+    )
+    assert timing["n_rows"] == 100
+    assert timing["n_physical_rows"] == 70
+    assert timing["n_physical_rows"] < timing["n_rows"]
+    assert timing["sample_s"] == 8.0  # rollout_s - write_s
+    assert timing["us_per_step"] == 8.0 / 70 * 1e6
+    assert timing["write_us_per_step"] == 2.0 / 70 * 1e6
+    assert timing["ms_per_event"] == 10.0 / 5 * 1e3
+    assert timing["device"] == "cpu" and timing["torch_threads"] == 4
+
+
+def test_build_rollout_timing_handles_zero_physical_rows_and_events():
+    timing = _build_rollout_timing(
+        setup_s=1.0,
+        rollout_s=1.0,
+        write_s=0.0,
+        n_rows=5,
+        termination_reason_counts={"escaped": 5},
+        n_seed_events=0,
+        device="cpu",
+        torch_threads=1,
+    )
+    assert timing["n_physical_rows"] == 0
+    assert timing["us_per_step"] is None
+    assert timing["write_us_per_step"] is None
+    assert timing["ms_per_event"] is None
+
+
 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)

tests/test_condor.py

@@ -252,6 +252,22 @@ def test_compute_one_from_run_dir(tmp_path: Path):
     assert list(partial.data["r"]) == ["rollout"]
 
 
+def test_timing_survives_plot_meta_to_compute_one(tmp_path: Path):
+    yaml_path = _write_inputs(tmp_path)
+    d = yaml.safe_load(yaml_path.read_text())
+    d["timing"] = {"us_per_step": 7.0, "write_us_per_step": 1.0}
+    yaml_path.write_text(yaml.safe_dump(d))
+
+    run_dir = _prep([yaml_path])
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    assert meta.rollouts[0]["plot_meta"]["timing"] == {"us_per_step": 7.0, "write_us_per_step": 1.0}
+
+    out = compute_one("eval_cost_per_step", run_dir)
+    reduced = Reduced(**Partial.load(out).data["reduced"])
+    assert reduced.kind == "bar"
+    assert reduced.payload["series"]["rollout"] == [7.0, 1.0, 8.0]
+
+
 def test_compute_reduced_explicit_paths(tmp_path: Path):
     run_dir = _prep([_write_inputs(tmp_path)])
     meta = RunMeta.load(run_dir / "run_meta.json")

tests/test_render.py

@@ -292,6 +292,20 @@ def test_render_one_of_each_kind(tmp_path: Path):
                 "ylabel": "frac",
             },
         ),
+        Reduced(
+            "cost",
+            "cost",
+            "bar",
+            "Cost",
+            "phase",
+            {
+                "labels": ["sample", "write", "total"],
+                "series": {"flow": [10.0, 1.0, 11.0]},
+                "reference": [5.0, 0.5, 5.5],
+                "ylabel": "us/step",
+                "log_y": True,
+            },
+        ),
         Reduced(
             "s",
             "species",

uv.lock

@@ -675,7 +675,7 @@ wheels = [
 
 [[package]]
 name = "giant"
-version = "0.3.15"
+version = "0.3.16"
 source = { editable = "." }
 dependencies = [
     { name = "numpy" },