chore: bump uv.lock and fix ruff 0.16 default-rule lint findings

uv.lock was stale (ty 0.0.50 -> 0.0.78, ruff 0.15 -> 0.16, polars, numpy,
typer, wandb, pytest, and others), all within existing pyproject.toml
bounds. ruff 0.16 widened its default rule selection, taking this repo
from 0 to 274 lint errors under the same config; --fix handled most of
it (import sorting, Optional[X] -> X | None, ...), and the remainder
(unused unpacked variables, dict()-as-literal, subprocess.run without
explicit check=, a couple of intentional broad excepts/naive datetimes)
were fixed or annotated by hand. Also fixes a real type-narrowing gap
ty 0.0.78 caught in test_config_consumed_keys.py's `or`-combined
isinstance check.

torch stays pinned to 2.3.x (deliberate, see CLAUDE.md); pyarrow's <25
ceiling is left as a separate decision.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TMdZFqXXig7i3XkirSUxef

giant/analysis/__init__.py

@@ -32,27 +32,27 @@ from giant.analysis.runtime_estimate import RUNTIME_SAFETY_MARGIN, estimate_runt
 from giant.analysis.sources import RolloutSpec, Side
 
 __all__ = [
-    "build_catalog",
-    "catalog_ids",
-    "get_spec",
+    "RUNTIME_SAFETY_MARGIN",
+    "Context",
     "LoadedRollout",
+    "Partial",
+    "Reduced",
+    "RolloutSpec",
     "RunMeta",
+    "Side",
     "SubmitConfig",
+    "build_catalog",
+    "build_context",
+    "catalog_ids",
     "compute_one",
     "compute_reduced",
     "derive_run_dir",
+    "estimate_runtime_s",
+    "get_spec",
     "load_rollout_yaml",
     "load_rollout_yamls",
     "merge_all",
     "merge_one",
     "prep",
     "write_submit",
-    "Context",
-    "build_context",
-    "Partial",
-    "Reduced",
-    "RolloutSpec",
-    "Side",
-    "RUNTIME_SAFETY_MARGIN",
-    "estimate_runtime_s",
 ]

giant/analysis/catalog.py

@@ -38,8 +38,8 @@ variable x grouping, secondaries, ...) into concrete specs.
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from dataclasses import asdict, dataclass
-from typing import Callable
 
 import numpy as np
 import polars as pl
@@ -101,7 +101,7 @@ class Bundle:
         reference,
         ctx: Context,
         chunk: tuple[int, int] | None = None,
-    ) -> "Bundle":
+    ) -> Bundle:
         """Open the reference + every rollout, optionally restricted to one event-disjoint chunk.
 
         ``chunk = (chunk_index, n_chunks)`` filters every side to

giant/analysis/condor.py

@@ -228,7 +228,7 @@ class RunMeta:
         Path(path).write_text(json.dumps(self.__dict__, indent=2))
 
     @classmethod
-    def load(cls, path: str | Path) -> "RunMeta":
+    def load(cls, path: str | Path) -> RunMeta:
         return cls(**json.loads(Path(path).read_text()))
 
 

giant/analysis/context.py

@@ -50,7 +50,7 @@ class Context:
         Path(path).write_text(json.dumps(asdict(self), indent=2))
 
     @classmethod
-    def load(cls, path: str | Path) -> "Context":
+    def load(cls, path: str | Path) -> Context:
         d = json.loads(Path(path).read_text())
         d["var_ranges"] = {k: tuple(v) for k, v in d["var_ranges"].items()}
         d["sec_energy_range"] = tuple(d["sec_energy_range"])

giant/analysis/grouping.py

@@ -46,7 +46,7 @@ def pdg_label(code: int) -> str:
 
 def material_label(name: str) -> str:
     """Display label for a Geant4 material, dropping the ``G4_`` prefix."""
-    return name[3:] if name.startswith("G4_") else name
+    return name.removeprefix("G4_")
 
 
 def energy_bin_edges(incident_E: np.ndarray, n_bins: int = 4) -> np.ndarray:

giant/analysis/reduced.py

@@ -46,7 +46,7 @@ class Reduced:
         Path(path).write_text(json.dumps(asdict(self)))
 
     @classmethod
-    def load(cls, path: str | Path) -> "Reduced":
+    def load(cls, path: str | Path) -> Reduced:
         return cls(**json.loads(Path(path).read_text()))
 
 
@@ -70,5 +70,5 @@ class Partial:
         Path(path).write_text(json.dumps(asdict(self)))
 
     @classmethod
-    def load(cls, path: str | Path) -> "Partial":
+    def load(cls, path: str | Path) -> Partial:
         return cls(**json.loads(Path(path).read_text()))

giant/analysis/render.py

@@ -27,8 +27,8 @@ from pathlib import Path
 
 import numpy as np
 import plotstyle as ps
-from matplotlib.colors import LogNorm
 import yaml
+from matplotlib.colors import LogNorm
 
 from giant.analysis.reduced import Reduced
 

giant/analysis/router_gating.py

@@ -58,10 +58,10 @@ _COLS = (
 
 @dataclass
 class _RouterHandle:
-    router: "torch.nn.Module"
+    router: torch.nn.Module
     pdg_map: dict[int, int]
     mat_map: dict[str, int]
-    cond_normalizer: "Normalizer"
+    cond_normalizer: Normalizer
     particle_conditioning: str
     material_conditioning: str
     router_type: str
@@ -233,7 +233,7 @@ def _gating_entry(checkpoint: str | Path | None, r_phys: pl.LazyFrame, t_phys: p
     return {"router_type": handle.router_type, "n_experts": handle.router.n_experts, **sides}
 
 
-def compute_router_gating(rollouts: dict[str, "RolloutSide"], t_phys: pl.LazyFrame, seed: int = 0) -> Reduced:
+def compute_router_gating(rollouts: dict[str, RolloutSide], t_phys: pl.LazyFrame, seed: int = 0) -> Reduced:
     """`Reduced` for the router-gating figure: one panel-pair per rollout with
     an enabled MoE router, or an explanatory note if none of them have one."""
     series = {}
@@ -289,7 +289,7 @@ def _specialization_entry(
     }
 
 
-def compute_router_specialization(rollouts: dict[str, "RolloutSide"], t_phys: pl.LazyFrame, seed: int = 0) -> Reduced:
+def compute_router_specialization(rollouts: dict[str, RolloutSide], t_phys: pl.LazyFrame, seed: int = 0) -> Reduced:
     """`Reduced` for the router-specialization figure, one curve per rollout with
     an enabled MoE router (see `_specialization_entry`)."""
     series = {}
@@ -331,7 +331,7 @@ def _share_by_pdg_entry(
 
 
 def compute_router_share_by_pdg(
-    rollouts: dict[str, "RolloutSide"], t_phys: pl.LazyFrame, top_pdgs: list[int], seed: int = 0
+    rollouts: dict[str, RolloutSide], t_phys: pl.LazyFrame, top_pdgs: list[int], seed: int = 0
 ) -> Reduced:
     """`Reduced` for the router expert-share-by-species figure, one panel-pair
     per rollout with an enabled MoE router."""
@@ -375,7 +375,7 @@ def _share_by_process_entry(checkpoint: str | Path | None, t_phys: pl.LazyFrame,
 
 
 def compute_router_share_by_process(
-    rollouts: dict[str, "RolloutSide"], t_phys: pl.LazyFrame, seed: int = 0, top_k: int = _TOP_K_PROCESS
+    rollouts: dict[str, RolloutSide], t_phys: pl.LazyFrame, seed: int = 0, top_k: int = _TOP_K_PROCESS
 ) -> Reduced:
     """Stacked-bar share of each physics process dispatched to each expert, one
     panel per rollout checkpoint with an enabled MoE router.

giant/analysis/type_embedding_distance.py

@@ -35,7 +35,7 @@ _NOTE_NOT_APPLICABLE = (
 )
 
 
-def compute_type_embedding_l1_distance(rollouts: dict[str, "RolloutSide"]) -> Reduced:
+def compute_type_embedding_l1_distance(rollouts: dict[str, RolloutSide]) -> Reduced:
     """`Reduced` for the type-embedding-distance figure: one series per rollout
     whose checkpoint populated the diagnostic, or an explanatory note if none did.
 

giant/cli.py

@@ -1,16 +1,15 @@
 from __future__ import annotations
 
-from collections import Counter
-from datetime import datetime, timezone
-from enum import Enum
 import math
-from pathlib import Path
 import re
-from typing import TYPE_CHECKING, Optional, cast
 import uuid as uuid_mod
+from collections import Counter
+from datetime import UTC, datetime
+from enum import Enum
+from pathlib import Path
+from typing import TYPE_CHECKING, Annotated, cast
 
 import typer
-from typing_extensions import Annotated
 
 if TYPE_CHECKING:
     import numpy as np
@@ -120,7 +119,7 @@ def _parse_router_axis_flags(specs: list[str]) -> dict[str, object]:
     return out
 
 
-def _parse_set_flags(specs: Optional[list[str]]) -> dict[str, object]:
+def _parse_set_flags(specs: list[str] | None) -> dict[str, object]:
     """Parse repeated `--set dotted.path=value` flags into a dict, typing
     each value with `_coerce_scalar` the same way a TOML file's native types
     would arrive. Validation against the inference-safe allowlist happens
@@ -196,7 +195,7 @@ def _write_prediction_ref(
         "output": str(out),
         "dataset": str(dataset_path),
         "checkpoint": str(checkpoint.resolve()),
-        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "timestamp": datetime.now(UTC).isoformat(),
     }
     if comment is not None:
         ref["comment"] = comment
@@ -285,52 +284,52 @@ class Weights(str, Enum):
 def train(
     data: Annotated[Path, typer.Argument(help="Parquet file or directory of parquet files")],
     config: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option("--config", "-c", help="TOML config file (overridden by explicit flags)"),
     ] = None,
     mode: Annotated[
-        Optional[Mode],
+        Mode | None,
         typer.Option("--mode", "-m", help="Generative model: flow matching or DDPM"),
     ] = None,
-    epochs: Annotated[Optional[int], typer.Option("--epochs", "-e")] = None,
+    epochs: Annotated[int | None, typer.Option("--epochs", "-e")] = None,
     batch_size: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--batch-size",
             "-b",
             help="Integer, or 'auto' to estimate from free GPU memory (cuda devices only)",
         ),
     ] = None,
-    lr: Annotated[Optional[float], typer.Option("--lr", "-l")] = None,
+    lr: Annotated[float | None, typer.Option("--lr", "-l")] = None,
     weight_decay: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--weight-decay", "-W", help="AdamW weight decay (default: 0.01)"),
     ] = None,
     ema_decay: Annotated[
-        Optional[float],
+        float | None,
         typer.Option(
             "--ema-decay",
             help="EMA decay for a shadow copy of the model weights, saved "
             "alongside the raw weights in checkpoints (0 disables; default: 0.9999)",
         ),
     ] = 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,
+    warmup_epochs: Annotated[int | None, typer.Option("--warmup-epochs", "-w")] = None,
+    hidden_dim: Annotated[int | None, typer.Option("--hidden-dim", "-H")] = None,
+    n_blocks: Annotated[int | None, typer.Option("--n-blocks", "-n")] = None,
+    emb_dim: Annotated[int | None, typer.Option("--emb-dim", "-E")] = None,
     dropout: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--dropout", "-d", help="Dropout probability in ResBlocks (default: 0.1)"),
     ] = None,
     stage1_generator: Annotated[
-        Optional[Mode],
+        Mode | None,
         typer.Option(
             "--stage1-generator",
             help="Stage 1's generative objective — overrides --mode for stage 1 only",
         ),
     ] = None,
     stage1_hidden_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage1-hidden-dim",
             help="Overrides --hidden-dim for stage 1 only (same effect today; "
@@ -339,15 +338,15 @@ def train(
         ),
     ] = None,
     stage1_n_res_blocks: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage1-n-res-blocks", help="Overrides --n-blocks for stage 1 only"),
     ] = None,
     stage1_dropout: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage1-dropout", help="Overrides --dropout for stage 1 only"),
     ] = None,
     stage2_generator: Annotated[
-        Optional[Mode],
+        Mode | None,
         typer.Option(
             "--stage2-generator",
             help="Stage 2's generative objective — overrides --mode for stage 2 "
@@ -356,19 +355,19 @@ def train(
         ),
     ] = None,
     stage2_hidden_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage2-hidden-dim", help="Stage 2 trunk width"),
     ] = None,
     stage2_n_res_blocks: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage2-n-res-blocks", help="Stage 2 trunk depth"),
     ] = None,
     stage2_dropout: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage2-dropout", help="Dropout inside stage 2's ResBlocks"),
     ] = None,
     stage2_decoder: Annotated[
-        Optional[Decoder],
+        Decoder | None,
         typer.Option(
             "--stage2-decoder",
             help="one_shot: predict all k_max secondary slots at once (v0.2 "
@@ -377,7 +376,7 @@ def train(
         ),
     ] = None,
     stage2_k_max: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage2-k-max",
             help="Maximum secondary slots (fixed width under one_shot, a "
@@ -385,14 +384,14 @@ def train(
         ),
     ] = None,
     stage2_context_dim: Annotated[
-        Optional[int],
+        int | None,
         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],
+        Stage1Context | None,
         typer.Option(
             "--stage2-stage1-context",
             help="What stage 2 conditions on during training: 'truth' (the "
@@ -402,7 +401,7 @@ def train(
         ),
     ] = None,
     conditioning: Annotated[
-        Optional[Conditioning],
+        Conditioning | None,
         typer.Option(
             "--conditioning",
             help="Input conditioning: continuous physical properties "
@@ -411,7 +410,7 @@ def train(
         ),
     ] = None,
     router: Annotated[
-        Optional[bool],
+        bool | None,
         typer.Option(
             "--router/--no-router",
             help="Route both stages through a mixture of small experts "
@@ -419,12 +418,12 @@ def train(
         ),
     ] = None,
     router_type: Annotated[
-        Optional[str],
+        str | None,
         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,
+    n_experts: Annotated[int | None, typer.Option("--n-experts", help="Number of routed experts")] = None,
     router_axis: Annotated[
-        Optional[list[str]],
+        list[str] | None,
         typer.Option(
             "--router-axis",
             help="Composed-router axis spec 'type:key=val,key=val' (repeatable; "
@@ -434,59 +433,59 @@ def train(
         ),
     ] = None,
     n_critic: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--n-critic",
             help="WGAN-GP (--mode wgan only): critic updates per generator update (default: 5)",
         ),
     ] = None,
     gp_weight: Annotated[
-        Optional[float],
+        float | None,
         typer.Option(
             "--gp-weight",
             help="WGAN-GP (--mode wgan only): gradient-penalty coefficient (default: 10.0)",
         ),
     ] = None,
     noise_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--noise-dim",
             help="WGAN (--mode wgan only): generator input noise-vector width (default: 64)",
         ),
     ] = None,
     critic_lr: Annotated[
-        Optional[float],
+        float | None,
         typer.Option(
             "--critic-lr",
             help="WGAN-GP (--mode wgan only): critic learning rate (default: same as --lr)",
         ),
     ] = None,
     stage1_n_critic: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage1-n-critic", help="Overrides --n-critic for stage 1 only"),
     ] = None,
     stage1_gp_weight: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage1-gp-weight", help="Overrides --gp-weight for stage 1 only"),
     ] = None,
     stage1_noise_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage1-noise-dim", help="Overrides --noise-dim for stage 1 only"),
     ] = None,
     stage1_critic_lr: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage1-critic-lr", help="Overrides --critic-lr for stage 1 only"),
     ] = None,
     stage2_n_critic: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--stage2-n-critic", help="Overrides --n-critic for stage 2 only"),
     ] = None,
     stage2_gp_weight: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage2-gp-weight", help="Overrides --gp-weight for stage 2 only"),
     ] = None,
     stage2_noise_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage2-noise-dim",
             help="Overrides --noise-dim for stage 2 only; under "
@@ -494,39 +493,39 @@ def train(
         ),
     ] = None,
     stage2_critic_lr: Annotated[
-        Optional[float],
+        float | None,
         typer.Option("--stage2-critic-lr", help="Overrides --critic-lr for stage 2 only"),
     ] = None,
     stage1_critic_hidden_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage1-critic-hidden-dim",
             help="WGAN-GP (--mode wgan only): critic width for stage 1 (default: same as generator's hidden_dim)",
         ),
     ] = None,
     stage1_critic_n_res_blocks: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage1-critic-n-res-blocks",
             help="WGAN-GP (--mode wgan only): critic depth for stage 1 (default: same as generator's n_res_blocks)",
         ),
     ] = None,
     stage2_critic_hidden_dim: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage2-critic-hidden-dim",
             help="WGAN-GP (--mode wgan only): critic width for stage 2 (default: same as generator's hidden_dim)",
         ),
     ] = None,
     stage2_critic_n_res_blocks: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--stage2-critic-n-res-blocks",
             help="WGAN-GP (--mode wgan only): critic depth for stage 2 (default: same as generator's n_res_blocks)",
         ),
     ] = None,
     stage1_init_from: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--stage1-init-from",
             help="Checkpoint .pt to load stage 1's weights from before training starts "
@@ -535,33 +534,33 @@ def train(
         ),
     ] = None,
     stage1_freeze: Annotated[
-        Optional[bool],
+        bool | None,
         typer.Option(
             "--stage1-freeze/--no-stage1-freeze",
             help="Never update stage 1's weights (requires --stage1-init-from, or --resume)",
         ),
     ] = None,
     stage2_init_from: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--stage2-init-from",
             help="Checkpoint .pt to load stage 2's weights from before training starts (gitea #42)",
         ),
     ] = None,
     stage2_freeze: Annotated[
-        Optional[bool],
+        bool | None,
         typer.Option(
             "--stage2-freeze/--no-stage2-freeze",
             help="Never update stage 2's weights (requires --stage2-init-from, or --resume)",
         ),
     ] = None,
-    val_fraction: Annotated[Optional[float], typer.Option("--val-fraction", "-f")] = None,
+    val_fraction: Annotated[float | None, typer.Option("--val-fraction", "-f")] = None,
     seed: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--seed", "-s", help="Random seed for reproducibility"),
     ] = None,
     validate_every: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--validate-every",
             "-v",
@@ -569,7 +568,7 @@ def train(
         ),
     ] = None,
     validate_steps: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--validate-steps",
             "-t",
@@ -578,7 +577,7 @@ def train(
         ),
     ] = None,
     max_val_batches: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--max-val-batches",
             help="Cap the per-epoch val-loss pass to N batches (0 = full val set every epoch; default: 200)",
@@ -609,7 +608,7 @@ def train(
         ),
     ] = False,
     out: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--out",
             "-o",
@@ -618,31 +617,31 @@ def train(
         ),
     ] = None,
     device: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--device", "-D", help="cpu | cuda | mps (default: auto)"),
     ] = None,
-    num_workers: Annotated[Optional[int], typer.Option("--num-workers", "-j")] = None,
+    num_workers: Annotated[int | None, typer.Option("--num-workers", "-j")] = None,
     resume: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option("--resume", "-r", help="Checkpoint .pt to resume training from"),
     ] = None,
     wandb: Annotated[
-        Optional[bool],
+        bool | None,
         typer.Option(
             "--wandb/--no-wandb",
             help="Log per-epoch training metrics to Weights & Biases (requires `uv sync --extra wandb`)",
         ),
     ] = None,
     wandb_project: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--wandb-project", help="W&B project name (default: giant)"),
     ] = None,
     wandb_run_name: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--wandb-run-name", help="W&B run name (default: out_dir name)"),
     ] = None,
     wandb_log_every: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--wandb-log-every",
             help="Log batch-level loss/grad_norm/lr to W&B every N optimizer "
@@ -650,7 +649,7 @@ def train(
         ),
     ] = None,
     precision: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--precision",
             help="Training-step autocast precision: 'fp32' (default) or "
@@ -664,7 +663,7 @@ def train(
     from giant.pipeline import run_train_job
 
     batch_size_auto = False
-    batch_size_value: Optional[int] = None
+    batch_size_value: int | None = None
     if batch_size is not None:
         if batch_size.strip().lower() == "auto":
             batch_size_auto = True
@@ -794,52 +793,52 @@ def train(
 @app.command("new-run")
 def new_run(
     config: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--config",
             "-c",
             help="Base TOML to start from (default: built-in defaults)",
         ),
     ] = None,
-    mode: Annotated[Optional[Mode], typer.Option("--mode", "-m")] = None,
-    epochs: Annotated[Optional[int], typer.Option("--epochs", "-e")] = None,
-    batch_size: Annotated[Optional[int], typer.Option("--batch-size", "-b")] = None,
-    lr: Annotated[Optional[float], typer.Option("--lr", "-l")] = 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")] = 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,
-    stage1_init_from: Annotated[Optional[Path], typer.Option("--stage1-init-from")] = None,
-    stage1_freeze: Annotated[Optional[bool], typer.Option("--stage1-freeze/--no-stage1-freeze")] = None,
-    stage2_init_from: Annotated[Optional[Path], typer.Option("--stage2-init-from")] = None,
-    stage2_freeze: Annotated[Optional[bool], typer.Option("--stage2-freeze/--no-stage2-freeze")] = 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,
-    router_axis: Annotated[Optional[list[str]], typer.Option("--router-axis")] = None,
+    mode: Annotated[Mode | None, typer.Option("--mode", "-m")] = None,
+    epochs: Annotated[int | None, typer.Option("--epochs", "-e")] = None,
+    batch_size: Annotated[int | None, typer.Option("--batch-size", "-b")] = None,
+    lr: Annotated[float | None, typer.Option("--lr", "-l")] = None,
+    hidden_dim: Annotated[int | None, typer.Option("--hidden-dim", "-H")] = None,
+    n_blocks: Annotated[int | None, typer.Option("--n-blocks", "-n")] = None,
+    emb_dim: Annotated[int | None, typer.Option("--emb-dim", "-E")] = None,
+    dropout: Annotated[float | None, typer.Option("--dropout", "-d")] = None,
+    stage1_generator: Annotated[Mode | None, typer.Option("--stage1-generator")] = None,
+    stage1_hidden_dim: Annotated[int | None, typer.Option("--stage1-hidden-dim")] = None,
+    stage1_n_res_blocks: Annotated[int | None, typer.Option("--stage1-n-res-blocks")] = None,
+    stage1_dropout: Annotated[float | None, typer.Option("--stage1-dropout")] = None,
+    stage2_generator: Annotated[Mode | None, typer.Option("--stage2-generator")] = None,
+    stage2_hidden_dim: Annotated[int | None, typer.Option("--stage2-hidden-dim")] = None,
+    stage2_n_res_blocks: Annotated[int | None, typer.Option("--stage2-n-res-blocks")] = None,
+    stage2_dropout: Annotated[float | None, typer.Option("--stage2-dropout")] = None,
+    stage2_decoder: Annotated[Decoder | None, typer.Option("--stage2-decoder")] = None,
+    stage2_k_max: Annotated[int | None, typer.Option("--stage2-k-max")] = None,
+    stage2_context_dim: Annotated[int | None, typer.Option("--stage2-context-dim")] = None,
+    stage2_stage1_context: Annotated[Stage1Context | None, typer.Option("--stage2-stage1-context")] = None,
+    stage1_init_from: Annotated[Path | None, typer.Option("--stage1-init-from")] = None,
+    stage1_freeze: Annotated[bool | None, typer.Option("--stage1-freeze/--no-stage1-freeze")] = None,
+    stage2_init_from: Annotated[Path | None, typer.Option("--stage2-init-from")] = None,
+    stage2_freeze: Annotated[bool | None, typer.Option("--stage2-freeze/--no-stage2-freeze")] = None,
+    conditioning: Annotated[Conditioning | None, typer.Option("--conditioning")] = None,
+    router: Annotated[bool | None, typer.Option("--router/--no-router")] = None,
+    router_type: Annotated[str | None, typer.Option("--router-type")] = None,
+    n_experts: Annotated[int | None, typer.Option("--n-experts")] = None,
+    router_axis: Annotated[list[str] | None, typer.Option("--router-axis")] = None,
     out: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option("--out", "-o", help="Run dir (default: auto from hyperparams)"),
     ] = None,
     comment: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--comment", help="Free-text note recorded in config.toml's meta section"),
     ] = None,
     data: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--data",
             help="Dataset path to fill in the printed next-step command (not stored in the config)",
@@ -931,7 +930,7 @@ def new_run(
         # 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_at": datetime.now(UTC).isoformat(timespec="seconds"),
         "created_by": "giant new-run",
     }
     if comment:
@@ -958,7 +957,7 @@ app.add_typer(model_app, name="model")
 @model_app.command("summary")
 def model_summary(
     config: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option("--config", "-c", help="TOML config file (default: built-in defaults)"),
     ] = None,
     pdg_vocab: Annotated[
@@ -1017,7 +1016,7 @@ def predict(
         ),
     ] = Coord.global_,
     out: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--out",
             "-o",
@@ -1050,11 +1049,11 @@ def predict(
         ),
     ] = Weights.raw,
     device: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--device", "-d", help="cpu | cuda | mps (default: auto)"),
     ] = None,
     comment: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--comment",
             "-m",
@@ -1062,7 +1061,7 @@ def predict(
         ),
     ] = None,
     set_: Annotated[
-        Optional[list[str]],
+        list[str] | None,
         typer.Option(
             "--set",
             help="Override a sampling-only model_config key on this checkpoint, "
@@ -1092,7 +1091,7 @@ def predict(
     from giant.sample import resolve_n_sec, sample_stage1, sample_stage2
 
     batch_size_auto = False
-    batch_size_value: Optional[int] = None
+    batch_size_value: int | None = None
     if batch_size.strip().lower() == "auto":
         batch_size_auto = True
     else:
@@ -1272,7 +1271,7 @@ def predict(
             # "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_E, sec_dir_world, _, _, sec_pdg_code, _l1_dist = decode_secondary_identity(
                 sec_decoder,
                 sec_cont,
                 sec_type,
@@ -1463,28 +1462,28 @@ def rollout(
     ] = Weights.raw,
     batch_size: Annotated[int, typer.Option("--batch-size", "-b", help="Tracks stepped per model forward")] = 4096,
     max_tracks_per_event: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--max-tracks-per-event",
             help="Safety cap on tracks per shower (sub-cap secondaries deposit in place)",
         ),
     ] = None,
     escape_threshold: Annotated[
-        Optional[float],
+        float | None,
         typer.Option(
             "--escape-threshold",
             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[int | None, typer.Option("--n-events", help="Cap number of seed events")] = None,
+    device: Annotated[str | None, typer.Option("--device", "-d", help="cpu | cuda | mps (auto)")] = None,
+    out: Annotated[Path | None, typer.Option("--out", "-o", help="Output steps parquet")] = None,
     seed: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--seed", help="Torch/numpy seed for reproducibility"),
     ] = None,
     set_: Annotated[
-        Optional[list[str]],
+        list[str] | None,
         typer.Option(
             "--set",
             help="Override a sampling-only model_config key on this checkpoint, "
@@ -1505,7 +1504,8 @@ 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, RolloutSummary, rollout as run_rollout
+    from giant.rollout import L1DistCollector, RolloutSummary
+    from giant.rollout import rollout as run_rollout
 
     _t_setup_start = time.perf_counter()
 
@@ -1640,7 +1640,7 @@ def rollout(
             "max_tracks_per_event": max_tracks_per_event,
             "escape_threshold": escape_threshold,
             "n_events": n_events,
-            "n_seed_events": int(len(seeds["event_id"])),
+            "n_seed_events": len(seeds["event_id"]),
             "weights": weights.value,
             "batch_size": batch_size,
             "device": str(_device),
@@ -1700,7 +1700,7 @@ def analyze_prep(
         ),
     ],
     label: Annotated[
-        Optional[list[str]],
+        list[str] | None,
         typer.Option(
             "--label",
             help="Series name for a rollout YAML, positionally matched to it — give none, "
@@ -1709,7 +1709,7 @@ def analyze_prep(
         ),
     ] = None,
     run_dir: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--run-dir",
             "-o",
@@ -1797,7 +1797,7 @@ def analyze_render(
 def analyze_metrics(
     run_dir: Annotated[Path, typer.Argument(help="Run directory containing metrics.csv (from `giant train`)")],
     out_dir: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--out",
             "-o",
@@ -1823,7 +1823,7 @@ def analyze_submit(
     ],
     accounting_group: Annotated[str, typer.Option("--accounting-group")],
     label: Annotated[
-        Optional[list[str]],
+        list[str] | None,
         typer.Option(
             "--label",
             help="Series name for a rollout YAML, positionally matched to it — give none, "
@@ -1832,7 +1832,7 @@ def analyze_submit(
         ),
     ] = None,
     run_dir: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--run-dir",
             "-o",

giant/config.py

@@ -9,7 +9,7 @@ import subprocess
 import sys
 import tomllib
 from dataclasses import dataclass, field
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from enum import Enum
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -80,7 +80,7 @@ class ConditioningAxisConfig:
     n_layers: int = 1
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "ConditioningAxisConfig":
+    def from_dict(cls, d: dict | None) -> ConditioningAxisConfig:
         d = d or {}
         return cls(
             type=d.get("type", "physical"),
@@ -106,7 +106,7 @@ class ConditioningConfig:
     material: ConditioningAxisConfig = field(default_factory=ConditioningAxisConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "ConditioningConfig":
+    def from_dict(cls, d: dict | None) -> ConditioningConfig:
         d = d or {}
         return cls(
             out_dim=d.get("out_dim", 128),
@@ -130,7 +130,7 @@ class FlowConfig:
     time_dim: int = 64
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "FlowConfig":
+    def from_dict(cls, d: dict | None) -> FlowConfig:
         d = d or {}
         return cls(time_dim=d.get("time_dim", 64))
 
@@ -144,7 +144,7 @@ class DdpmConfig:
     n_steps: int = 1000
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "DdpmConfig":
+    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))
 
@@ -166,7 +166,7 @@ class Stage1WganConfig:
     critic_n_res_blocks: int = 0
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage1WganConfig":
+    def from_dict(cls, d: dict | None) -> Stage1WganConfig:
         d = d or {}
         return cls(
             noise_dim=d.get("noise_dim", 64),
@@ -198,7 +198,7 @@ class Stage2WganConfig(Stage1WganConfig):
     gumbel_tau_end: float = 0.1
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage2WganConfig":
+    def from_dict(cls, d: dict | None) -> Stage2WganConfig:
         d = d or {}
         return cls(
             noise_dim=d.get("noise_dim", 64),
@@ -293,7 +293,7 @@ class RouterConfig:
     extra: dict = field(default_factory=dict)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "RouterConfig":
+    def from_dict(cls, d: dict | None) -> RouterConfig:
         d = d or {}
         return cls(
             enabled=d.get("enabled", False),
@@ -360,7 +360,7 @@ class TrunkConfig:
     block_conditioning: str = "add"
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "TrunkConfig":
+    def from_dict(cls, d: dict | None) -> TrunkConfig:
         d = d or {}
         return cls(type=d.get("type", "resmlp"), block_conditioning=d.get("block_conditioning", "add"))
 
@@ -377,7 +377,7 @@ class Stage2RouterConfig(RouterConfig):
     tie_to_stage1: bool = False
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage2RouterConfig":
+    def from_dict(cls, d: dict | None) -> Stage2RouterConfig:
         d = d or {}
         known = _ROUTER_KNOWN_KEYS | {"tie_to_stage1"}
         return cls(
@@ -447,7 +447,7 @@ class NSecConfig:
     sampling: str = "greedy"
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "NSecConfig":
+    def from_dict(cls, d: dict | None) -> NSecConfig:
         d = d or {}
         return cls(
             mode=d.get("mode", "head"),
@@ -498,7 +498,7 @@ class ParticleTypeConfig:
     class_weighting: str = "none"
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "ParticleTypeConfig":
+    def from_dict(cls, d: dict | None) -> ParticleTypeConfig:
         d = d or {}
         return cls(
             target=d.get("target", "onehot"),
@@ -537,7 +537,7 @@ class AutoregressiveConfig:
     attn_n_layers: int = 2
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "AutoregressiveConfig":
+    def from_dict(cls, d: dict | None) -> AutoregressiveConfig:
         d = d or {}
         return cls(
             order=d.get("order", "energy_desc"),
@@ -576,7 +576,7 @@ class HeadConfig:
     depth: int = 2  # matches build_mlp_head's depth
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "HeadConfig":
+    def from_dict(cls, d: dict | None) -> HeadConfig:
         d = d or {}
         return cls(hidden_ratio=d.get("hidden_ratio", 0.5), depth=d.get("depth", 2))
 
@@ -593,7 +593,7 @@ class Stage1HeadsConfig:
     n_sec: HeadConfig = field(default_factory=HeadConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage1HeadsConfig":
+    def from_dict(cls, d: dict | None) -> Stage1HeadsConfig:
         d = d or {}
         return cls(n_sec=HeadConfig.from_dict(d.get("n_sec")))
 
@@ -611,7 +611,7 @@ class Stage2HeadsConfig:
     type: HeadConfig = field(default_factory=HeadConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage2HeadsConfig":
+    def from_dict(cls, d: dict | None) -> Stage2HeadsConfig:
         d = d or {}
         return cls(n_sec=HeadConfig.from_dict(d.get("n_sec")), type=HeadConfig.from_dict(d.get("type")))
 
@@ -659,7 +659,7 @@ class Stage1ModelConfig:
     heads: Stage1HeadsConfig = field(default_factory=Stage1HeadsConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage1ModelConfig":
+    def from_dict(cls, d: dict | None) -> Stage1ModelConfig:
         d = d or {}
         return cls(
             active=d.get("active", True),
@@ -745,7 +745,7 @@ class Stage2ModelConfig:
     heads: Stage2HeadsConfig = field(default_factory=Stage2HeadsConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "Stage2ModelConfig":
+    def from_dict(cls, d: dict | None) -> Stage2ModelConfig:
         d = d or {}
         return cls(
             active=d.get("active", True),
@@ -839,7 +839,7 @@ class TrainConfig:
     precision: str = "fp32"
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "TrainConfig":
+    def from_dict(cls, d: dict | None) -> TrainConfig:
         d = d or {}
         return cls(
             epochs=d.get("epochs", 100),
@@ -895,7 +895,7 @@ class GiantConfig:
     train: TrainConfig = field(default_factory=TrainConfig)
 
     @classmethod
-    def from_dict(cls, d: dict | None) -> "GiantConfig":
+    def from_dict(cls, d: dict | None) -> GiantConfig:
         d = d or {}
         return cls(
             conditioning=ConditioningConfig.from_dict(d.get("conditioning")),
@@ -938,7 +938,7 @@ def leaf_paths(node: dict, prefix: str = "") -> list[str]:
 def git_hash() -> str:
     try:
         return subprocess.check_output(["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL).decode().strip()
-    except Exception:
+    except Exception:  # noqa: BLE001 - any failure (no git, no repo, ...) degrades to "unknown"
         return "unknown"
 
 
@@ -1852,7 +1852,7 @@ def default_out_dir_name(cfg: dict, now: datetime | None = None) -> str:
     name unboundedly. This name doubles as the run's W&B id (see
     giant.training), which is the reason a timestamp is always included.
     """
-    now = now or datetime.now()
+    now = now or datetime.now()  # noqa: DTZ005 - human-readable local wall-clock time for run/W&B naming, not stored
     tokens = []
     overflow = []
     for label, candidate in _OUT_DIR_NAME_CANDIDATES:
@@ -1951,7 +1951,7 @@ def build_run_meta(
         "config_version": CONFIG_VERSION,
         "git_hash": git_hash(),
         "seed": seed,
-        "timestamp_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "timestamp_utc": datetime.now(UTC).isoformat(timespec="seconds"),
         "python_version": sys.version.split()[0],
         "torch_version": torch.__version__,
         "command": " ".join(sys.argv),

giant/data/loader.py

@@ -1,6 +1,7 @@
+from collections.abc import Iterator, Mapping
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Iterator, Mapping
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 import polars as pl

giant/data/setup_cache.py

@@ -172,7 +172,7 @@ class NormalizerEntry:
         }
 
     @classmethod
-    def from_json(cls, d: dict) -> "NormalizerEntry":
+    def from_json(cls, d: dict) -> NormalizerEntry:
         return cls(
             cond_norm=Normalizer.from_dict(d["cond_norm"]),
             tgt_norm=Normalizer.from_dict(d["tgt_norm"]),
@@ -194,7 +194,7 @@ class SetupCache:
     """Keyed by `topn_key(axis, n_classes)`."""
 
     @classmethod
-    def empty(cls, files: list[Path]) -> "SetupCache":
+    def empty(cls, files: list[Path]) -> SetupCache:
         return cls(fingerprint=fingerprint_files(files))
 
     def to_json(self) -> dict:
@@ -222,7 +222,7 @@ class SetupCache:
         return d
 
     @classmethod
-    def from_json(cls, d: dict) -> "SetupCache":
+    def from_json(cls, d: dict) -> SetupCache:
         vocab = None
         if "vocab" in d:
             pdg_map = {int(k): v for k, v in d["vocab"]["pdg_map"].items()}
@@ -247,7 +247,7 @@ class SetupCache:
             topn_maps=topn_maps,
         )
 
-    def merge(self, other: "SetupCache") -> "SetupCache":
+    def merge(self, other: SetupCache) -> SetupCache:
         """Union of both caches; `other`'s populated fields win on a shared key.
 
         Used by `save` to combine freshly-computed sections with whatever a

giant/geometry.py

@@ -23,9 +23,10 @@ install stays lean.
 
 from __future__ import annotations
 
+from collections.abc import Iterable
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Iterable
+from typing import Any
 
 import numpy as np
 import polars as pl
@@ -155,7 +156,7 @@ class GeometryOracle:
         )
 
     @classmethod
-    def load(cls, path: str | Path) -> "GeometryOracle":
+    def load(cls, path: str | Path) -> GeometryOracle:
         _require_sklearn()
         import joblib
 
@@ -353,7 +354,7 @@ def _fit_slab_lookup(
         radius_max=radius_max,
     )
     info = {
-        "n_segments": int(len(materials)),
+        "n_segments": len(materials),
         "z_range": (z_min, z_max),
         "median_z_spacing": median_spacing,
         "radius_max": radius_max,
@@ -412,7 +413,7 @@ def build_geometry_oracle(
                 "method": "slab",
                 "depth_axis": depth_axis,
                 "n_bins": n_bins,
-                "n_reference_points": int(len(pos)),
+                "n_reference_points": len(pos),
                 "escape_factor": escape_factor,
                 "n_files": len(files),
                 **info,
@@ -458,7 +459,7 @@ def build_geometry_oracle(
         metadata={
             "method": method,
             "k": k,
-            "n_reference_points": int(len(X)),
+            "n_reference_points": len(X),
             "median_nn_dist": median_nn,
             "escape_factor": escape_factor,
             "n_files": len(files),

giant/model/builders.py

@@ -1,7 +1,7 @@
 """Factories: `build_models`/`build_critics` assemble the top-level stage
 models from a config dict (issues.md Issue 8)."""
 
-import torch.nn as nn
+from torch import nn
 
 from giant.config import ConditioningConfig, Stage1ModelConfig, Stage2ModelConfig
 from giant.constants import X_DIM

giant/model/encoders.py

@@ -2,8 +2,8 @@
 identity (issues.md Issue 8)."""
 
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 from giant.cond_layout import CondLayout
 from giant.config import ConditioningAxisConfig

giant/model/history.py

@@ -7,7 +7,7 @@ import inspect
 from typing import cast
 
 import torch
-import torch.nn as nn
+from torch import nn
 
 
 class HistoryEncoder(nn.Module):
@@ -197,7 +197,7 @@ class AttentionHistory(HistoryEncoder):
         return self.in_proj(x)
 
     def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor:
-        B, K, _ = feat.shape
+        _, 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:

giant/model/layers.py

@@ -4,7 +4,7 @@ no dependency on any other `giant.model` submodule (issues.md Issue 8)."""
 import math
 
 import torch
-import torch.nn as nn
+from torch import nn
 
 
 class SinusoidalEmbedding(nn.Module):

giant/model/models.py

@@ -2,7 +2,7 @@
 `CriticModel` — composed from encoders/trunks/history (issues.md Issue 8)."""
 
 import torch
-import torch.nn as nn
+from torch import nn
 
 from giant.config import ConditioningAxisConfig, HeadConfig, ParticleTypeConfig
 from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_DIM, SEC_SLOT_DIM, X_DIM

giant/model/network.py

@@ -79,9 +79,13 @@ from giant.model.trunks import (
 )
 
 __all__ = [
+    "BLOCK_REGISTRY",
+    "HISTORY_REGISTRY",
+    "OBJECTIVE_REGISTRY",
+    "ROUTER_REGISTRY",
+    "TRUNK_REGISTRY",
     "AdaLNResBlock",
     "AttentionHistory",
-    "BLOCK_REGISTRY",
     "ComposedRouter",
     "ConditionEncoder",
     "ContextAdapter",
@@ -91,17 +95,14 @@ __all__ = [
     "ExpertTrunk",
     "FilmResBlock",
     "FlowObjective",
-    "HISTORY_REGISTRY",
     "HistoryEncoder",
     "LinearTrunk",
     "MarkovHistory",
     "NoHistory",
     "NoneRouter",
-    "OBJECTIVE_REGISTRY",
     "Objective",
     "PdgRouter",
     "ProcessRouter",
-    "ROUTER_REGISTRY",
     "ResBlock",
     "RoutedTrunk",
     "Router",
@@ -110,7 +111,6 @@ __all__ = [
     "Stage2Autoregressive",
     "Stage2OneShot",
     "StageModel",
-    "TRUNK_REGISTRY",
     "Trunk",
     "WganObjective",
     "_CausalAttnBlock",

giant/model/routers.py

@@ -8,8 +8,8 @@ import re
 from collections.abc import Sequence
 
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 from giant.cond_layout import CondLayout
 from giant.constants import COND_DIM
@@ -398,7 +398,7 @@ def _build_router_from_cfg(
     """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)
+    shared_vocab = {"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)

giant/model/schedule.py

@@ -14,7 +14,7 @@ class CosineSchedule:
         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))
+        self.alphas = torch.from_numpy(1.0 - betas)
         self.alpha_bars = torch.from_numpy(alpha_bars[1:])
 
     def to(self, device: torch.device) -> "CosineSchedule":

giant/model/summary.py

@@ -35,7 +35,7 @@ finding those two tests independently converge on.
 import copy
 from dataclasses import dataclass, field
 
-import torch.nn as nn
+from torch import nn
 
 from giant.config import INFERENCE_OVERRIDES, _get_path, _set_path, leaf_paths
 from giant.model.builders import build_critics, build_models
@@ -224,7 +224,7 @@ def summarize_model(cfg: dict, pdg_vocab: int, mat_vocab: int) -> ModelSummary:
             _set_path(probe_cfg, path, candidate)
             try:
                 changed = _fingerprint(_built_modules(probe_cfg, pdg_vocab, mat_vocab)) != baseline_fp
-            except Exception:
+            except Exception:  # noqa: BLE001 - a perturbation that fails to even build counts as "consumed"
                 changed = True
             if changed:
                 break

giant/model/trunks.py

@@ -10,7 +10,7 @@ free — no separate "routed transformer trunk" class needed.
 """
 
 import torch
-import torch.nn as nn
+from torch import nn
 
 from giant.model.layers import build_block
 from giant.model.routers import Router

giant/model/wgan.py

@@ -1,4 +1,4 @@
-from typing import Callable
+from collections.abc import Callable
 
 import torch
 

giant/particles.py

@@ -19,7 +19,7 @@ free-running history representation stays unsnapped — see
 
 from __future__ import annotations
 
-from functools import lru_cache
+from functools import cache
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -42,7 +42,7 @@ _CHARGE_WEIGHT = 50.0
 _LOG_EPS = 1e-8
 
 
-@lru_cache(maxsize=None)
+@cache
 def particle_mass_charge(pdg: int) -> tuple[float, float]:
     """Return (mass_MeV, charge_e) for a raw PDG code.
 
@@ -135,7 +135,7 @@ def invert_dense_map(m: dict[int, int]) -> dict[int, int]:
 
 def decode_topn_class(
     class_idx: np.ndarray,
-    topn_map: "TopNMap",
+    topn_map: TopNMap,
     n_classes: int,
     other_policy: str = "sample",
     rng: np.random.Generator | None = None,

giant/pipeline.py

@@ -13,6 +13,7 @@ from giant.constants import (
     X_DIM,
 )
 from giant.data import setup_cache
+from giant.data.dataset import StreamingStepsDataset, make_event_split
 from giant.data.loader import (
     TopNMap,
     _topn_plus_other_map,
@@ -23,13 +24,12 @@ from giant.data.loader import (
 from giant.data.scan import MetadataScan, ScanRequest, scan_metadata
 from giant.data.transforms import (
     Normalizer,
-    build_features,
-    _WelfordAccumulator,
     _ReservoirSampler,
+    _WelfordAccumulator,
+    build_features,
     sorted_membership,
 )
-from giant.data.dataset import make_event_split, StreamingStepsDataset
-from giant.model.network import build_models, build_critics, resolve_type_n_classes
+from giant.model.network import build_critics, build_models, resolve_type_n_classes
 from giant.training import train as run_training
 
 

giant/rollout.py

@@ -17,7 +17,8 @@ treated as detector leakage and not deposited.
 from __future__ import annotations
 
 from collections import Counter
-from typing import TYPE_CHECKING, Callable, TypedDict
+from collections.abc import Callable
+from typing import TYPE_CHECKING, TypedDict
 
 import numpy as np
 import torch
@@ -117,7 +118,7 @@ def decode_secondary_identity(
     pre_dir: np.ndarray,
     sec_phys_norm: Normalizer,
     pdg_map: dict[int, int],
-    sec_type_topn_map: "TopNMap | None",
+    sec_type_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]:
@@ -390,34 +391,34 @@ def _terminal_rows(tr: dict[str, np.ndarray], sel: np.ndarray, reason: str, edep
     pos = tr["pre_pos"][sel]
     dir_ = tr["pre_dir"][sel]
     n = int(sel.sum())
-    return dict(
-        event_id=tr["event_id"][sel],
-        track_id=tr["track_id"][sel],
-        parent_id=tr["parent_id"][sel],
-        generation=tr["generation"][sel],
-        step_no=tr["step_in_track"][sel],
-        pdg=tr["pdg"][sel],
-        pre_x=pos[:, 0],
-        pre_y=pos[:, 1],
-        pre_z=pos[:, 2],
-        pre_E=tr["pre_E"][sel],
-        pre_dx=dir_[:, 0],
-        pre_dy=dir_[:, 1],
-        pre_dz=dir_[:, 2],
-        post_x=pos[:, 0],
-        post_y=pos[:, 1],
-        post_z=pos[:, 2],
-        post_E=np.zeros(n),
-        post_dx=dir_[:, 0],
-        post_dy=dir_[:, 1],
-        post_dz=dir_[:, 2],
-        edep=np.asarray(edep, dtype=np.float64).reshape(n),
-        step_length=np.zeros(n),
-        material=tr.get("_material", np.full(len(sel), "", dtype=object))[sel],
-        layer_id=tr.get("_layer_id", np.zeros(len(sel), dtype=np.int64))[sel],
-        n_sec_pred=np.zeros(n, dtype=np.int64),
-        termination_reason=np.full(n, reason, dtype=object),
-    )
+    return {
+        "event_id": tr["event_id"][sel],
+        "track_id": tr["track_id"][sel],
+        "parent_id": tr["parent_id"][sel],
+        "generation": tr["generation"][sel],
+        "step_no": tr["step_in_track"][sel],
+        "pdg": tr["pdg"][sel],
+        "pre_x": pos[:, 0],
+        "pre_y": pos[:, 1],
+        "pre_z": pos[:, 2],
+        "pre_E": tr["pre_E"][sel],
+        "pre_dx": dir_[:, 0],
+        "pre_dy": dir_[:, 1],
+        "pre_dz": dir_[:, 2],
+        "post_x": pos[:, 0],
+        "post_y": pos[:, 1],
+        "post_z": pos[:, 2],
+        "post_E": np.zeros(n),
+        "post_dx": dir_[:, 0],
+        "post_dy": dir_[:, 1],
+        "post_dz": dir_[:, 2],
+        "edep": np.asarray(edep, dtype=np.float64).reshape(n),
+        "step_length": np.zeros(n),
+        "material": tr.get("_material", np.full(len(sel), "", dtype=object))[sel],
+        "layer_id": tr.get("_layer_id", np.zeros(len(sel), dtype=np.int64))[sel],
+        "n_sec_pred": np.zeros(n, dtype=np.int64),
+        "termination_reason": np.full(n, reason, dtype=object),
+    }
 
 
 @torch.no_grad()
@@ -442,14 +443,14 @@ def rollout(
     on_chunk: Callable[[dict[str, np.ndarray]], None] | None = None,
     particle_conditioning: str = "embedding",
     material_conditioning: str = "embedding",
-    pdg_topn_map: "TopNMap | None" = None,
-    mat_topn_map: "TopNMap | None" = None,
-    sec_type_topn_map: "TopNMap | None" = None,
+    pdg_topn_map: TopNMap | None = None,
+    mat_topn_map: TopNMap | None = None,
+    sec_type_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,
+    l1_dist_collector: L1DistCollector | None = None,
 ) -> dict[str, np.ndarray] | RolloutSummary:
     """Run showers to completion.
 

giant/tools/bump_dataset_version.py

@@ -82,7 +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, check=False)
     except (OSError, subprocess.SubprocessError):
         # OSError (e.g. git not on PATH) and subprocess.SubprocessError
         # (e.g. TimeoutExpired) are unrelated hierarchies — TimeoutExpired
@@ -604,7 +604,7 @@ def _run_bump(
     if not root_path.is_dir():
         raise SystemExit(f"error: {root_path} is not a directory")
 
-    date = date or dt.date.today().isoformat()
+    date = date or dt.date.today().isoformat()  # noqa: DTZ011 - local calendar date for the dataset-version log, not stored
     by = by if by is not None else _git_user_name()
     if gen is None:
         new_dirs, log_line = plan_bump_gen(root_path, kind, reason, by, date, to)

giant/tools/check_migration_v02_v03.py

@@ -31,9 +31,9 @@ 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
+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
 
 
 def _random_batch(model_config: dict, batch: int, seed: int):

giant/tools/create_root_files.py

@@ -28,8 +28,8 @@ import subprocess
 import sys
 import uuid
 import zlib
-from dataclasses import dataclass
 from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass
 from pathlib import Path
 
 # Must match giant/tools/bump_dataset_version.py's GEN_RE.
@@ -147,7 +147,7 @@ def run_job(
     cmd = build_cmd(executable, job, events_per_file, energy_gev)
 
     env = dict(os.environ, MINICALOSIM_SEED=str(job_seed(kind, gen, job, energy_gev)))
-    result = subprocess.run(cmd, cwd=workdir, capture_output=True, text=True, env=env)
+    result = subprocess.run(cmd, cwd=workdir, capture_output=True, text=True, env=env, check=False)
 
     if result.returncode != 0:
         return JobResult(

giant/tools/dwarf.py

@@ -10,10 +10,9 @@ from __future__ import annotations
 import os
 from enum import Enum
 from pathlib import Path
-from typing import Optional
+from typing import Annotated
 
 import typer
-from typing_extensions import Annotated
 
 from giant.config import Conditioning
 
@@ -73,7 +72,7 @@ class PoolType(str, Enum):
 def convert(
     root_files: Annotated[list[Path], typer.Argument(help="Input ROOT file(s)")],
     output: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--output",
             "-o",
@@ -108,7 +107,7 @@ def convert(
         ),
     ] = _DATASET_ROOT_DEFAULT,
     schema: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--schema",
             help="Schema tag to write parquets under, e.g. schema2 (only used "
@@ -192,13 +191,13 @@ def migrate(
 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,
+    by: Annotated[str | None, typer.Option("--by", help="Attribution (default: git user.name)")] = None,
     date: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--date", help="Override date (default: today, ISO)"),
     ] = None,
     to: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--to",
             metavar="genN",
@@ -227,13 +226,13 @@ 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,
+    by: Annotated[str | None, typer.Option("--by", help="Attribution (default: git user.name)")] = None,
     date: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--date", help="Override date (default: today, ISO)"),
     ] = None,
     to: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--to",
             metavar="schemaN",
@@ -272,7 +271,7 @@ def status(
 def update_manifest(
     manifests: Annotated[list[Path], typer.Argument(help="One or more .manifest files to update")],
     schema: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--schema",
             metavar="schemaN",
@@ -280,7 +279,7 @@ def update_manifest(
         ),
     ] = None,
     gen: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--gen", metavar="genN", help="Target gen tag (default: keep existing gen)"),
     ] = None,
     execute: Annotated[
@@ -298,11 +297,11 @@ def update_manifest(
 def create_manifest(
     files: Annotated[list[Path], typer.Argument(help="Parquet files to include")],
     output: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option("--output", "-o", help="Explicit path for the new .manifest file"),
     ] = None,
     pool: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(
             "--pool",
             metavar="DETECTOR",
@@ -310,7 +309,7 @@ def create_manifest(
         ),
     ] = None,
     type_: Annotated[
-        Optional[PoolType],
+        PoolType | None,
         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,
@@ -459,7 +458,7 @@ def warm_cache(
         typer.Argument(help="Parquet file, directory, or .manifest — same as `giant train`'s"),
     ],
     config: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             "--config",
             "-c",
@@ -469,7 +468,7 @@ def warm_cache(
         ),
     ] = None,
     val_fraction: Annotated[
-        Optional[float],
+        float | None,
         typer.Option(
             "--val-fraction",
             "-f",
@@ -477,7 +476,7 @@ def warm_cache(
         ),
     ] = None,
     seed: Annotated[
-        Optional[int],
+        int | None,
         typer.Option(
             "--seed",
             "-s",
@@ -485,7 +484,7 @@ def warm_cache(
         ),
     ] = None,
     particle_conditioning: Annotated[
-        Optional[Conditioning],
+        Conditioning | None,
         typer.Option(
             "--particle-conditioning",
             help="Must match the `giant train` run(s)' conditioning.particle.type to warm for. "
@@ -493,7 +492,7 @@ def warm_cache(
         ),
     ] = None,
     material_conditioning: Annotated[
-        Optional[Conditioning],
+        Conditioning | None,
         typer.Option(
             "--material-conditioning",
             help="Must match the `giant train` run(s)' conditioning.material.type "
@@ -502,7 +501,7 @@ def warm_cache(
         ),
     ] = None,
     router: Annotated[
-        Optional[bool],
+        bool | None,
         typer.Option(
             "--router/--no-router",
             help="Warm the process vocabulary too (only takes effect with --router-type process). "
@@ -510,11 +509,11 @@ def warm_cache(
         ),
     ] = None,
     router_type: Annotated[
-        Optional[str],
+        str | None,
         typer.Option("--router-type", help="Router implementation name. Not allowed together with --config"),
     ] = None,
     n_experts: Annotated[
-        Optional[int],
+        int | None,
         typer.Option("--n-experts", help="Number of routed experts. Not allowed together with --config"),
     ] = None,
     rebuild: Annotated[

giant/tools/hparam_scan.py

@@ -144,7 +144,7 @@ def run_hparam_scan(
         start = time.monotonic()
         try:
             with open(out_dir / "train.log", "a") as log:
-                subprocess.run(cmd, env=env, stdout=log, stderr=subprocess.STDOUT)
+                subprocess.run(cmd, env=env, stdout=log, stderr=subprocess.STDOUT, check=False)
         except KeyboardInterrupt:
             print(
                 f"\ninterrupted during {name} — re-run this script to resume "

giant/tools/migrate_geant_steps.py

@@ -32,7 +32,7 @@ MANIFEST_SUFFIX = ".manifest"
 # since today's pool assignment is encoded only by *which folder a file's
 # parquet was copied into* — not by anything in the filename itself.
 POOL_ASSIGNMENT: dict[str, dict[str, range | list[int]]] = {
-    "pbwo4": {"full": range(0, 6), "holdout": range(6, 10)},
+    "pbwo4": {"full": range(6), "holdout": range(6, 10)},
     "sampling_fe_scint": {"dev": [0], "full": [1, 2], "holdout": [3]},
     "sampling_pb_lar": {"dev": [0], "full": [1, 2], "holdout": [3]},
     "sampling_pb_scint": {"dev": [0], "full": [1, 2], "holdout": [3]},

giant/tools/steps_to_parquet_parallel.py

@@ -106,7 +106,7 @@ def _convert_one(
     if output_path is not None:
         output_path.parent.mkdir(parents=True, exist_ok=True)
         cmd += ["--output", str(output_path)]
-    result = subprocess.run(cmd, capture_output=True, text=True)
+    result = subprocess.run(cmd, capture_output=True, text=True, check=False)
     return root_file, result.returncode, result.stdout, result.stderr
 
 

giant/training/__init__.py

@@ -6,8 +6,8 @@ that tests and tooling construct directly.
 """
 
 from giant.training.checkpoint import build_checkpoint, init_stages_from_checkpoints, load_checkpoint
-from giant.training.metrics import MetricsCollector, MetricSpec
 from giant.training.loop import train
+from giant.training.metrics import MetricsCollector, MetricSpec
 from giant.training.trainers import (
     FlowDDPMStageTrainer,
     StageSpec,

giant/training/loop.py

@@ -9,9 +9,10 @@
 import os
 import signal
 import time
+from collections.abc import Callable
 from pathlib import Path
 from types import FrameType
-from typing import Callable
+from typing import Self
 
 import numpy as np
 import torch
@@ -47,7 +48,7 @@ class _GracefulShutdown:
             Callable[[int, FrameType | None], object] | signal.Handlers | int | None,
         ] = {}
 
-    def __enter__(self) -> "_GracefulShutdown":
+    def __enter__(self) -> Self:
         for sig in _CATCHABLE_SIGNALS:
             self._previous[sig] = signal.getsignal(sig)
             signal.signal(sig, self._handle)

giant/training/metrics.py

@@ -188,7 +188,7 @@ class MetricsCollector:
         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._file = open(metrics_path, "a" if append else "w", newline="")  # noqa: SIM115 - kept open for the object's lifetime, closed in .close()
         self._writer = csv.DictWriter(self._file, fieldnames=self.fieldnames)
         if not append:
             self._writer.writeheader()

giant/training/plots.py

@@ -47,7 +47,7 @@ class MetricsTable:
     columns: dict[str, list[float]]
 
     @classmethod
-    def load(cls, path: str | Path) -> "MetricsTable":
+    def load(cls, path: str | Path) -> MetricsTable:
         with open(path, newline="") as f:
             rows = list(csv.DictReader(f))
         epochs = [int(float(r["epoch"])) for r in rows]

giant/training/trainers.py

@@ -20,7 +20,7 @@ from typing import NamedTuple
 
 import torch
 import torch.nn.functional as F
-import torch.optim as optim
+from torch import optim
 
 from giant.config import ParticleTypeConfig, Stage1ModelConfig, Stage2ModelConfig, TrainConfig
 from giant.constants import CONT_SLOT_DIM
@@ -333,7 +333,7 @@ class StageTrainer:
         #: "sampled"` — the stage-1 `StageTrainer` this (stage-2) trainer
         #: draws its context sample from. `None` for stage 1 itself, and for
         #: stage 2 under "truth".
-        self.stage1_source: "StageTrainer | None" = None
+        self.stage1_source: StageTrainer | None = None
 
     def attach_stage1(self, stage1_trainer: "StageTrainer") -> None:
         """Wires this (stage-2) trainer to the stage-1 trainer it should

tests/legacy/network_v02_snapshot.py

@@ -17,8 +17,8 @@ import re
 from collections.abc import Sequence
 
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
+from torch import nn
 
 from giant.constants import (
     COND_DIM,
@@ -949,7 +949,7 @@ def _check_router_conditioning_compat(router_types: list[str], conditioning: str
 
 
 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)
+    shared_vocab = {"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)
@@ -977,15 +977,15 @@ def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]:
     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"),
-        )
+        shared = {
+            "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),

tests/test_amp.py

@@ -5,12 +5,12 @@ from pathlib import Path
 
 import pytest
 import torch
+from test_train import _base_cfg, _run_train
 
 from giant.model.routers import EnergyRouter
 from giant.model.wgan import gradient_penalty
 from giant.training.amp import resolve_autocast
 from giant.training.stage2_inputs import _remaining_energy_fraction
-from test_train import _base_cfg, _run_train
 
 # ---------------------------------------------------------------------------
 # resolve_autocast

tests/test_analysis_reduce.py

@@ -184,7 +184,7 @@ def test_weighted_profile_matches_manual_bincount():
     ea = R.entry_axis(lf)
     lf2 = R.attach_entry_axis(lf, ea)
     edges = np.linspace(0.0, 3.0, 4)  # depth bins along +z
-    mean, std = R.weighted_profile(lf2, R.depth_expr(), edges, pl.col("edep"))
+    mean, _ = R.weighted_profile(lf2, R.depth_expr(), edges, pl.col("edep"))
     assert mean.shape == (3,)
     # totals conserved: sum over bins == mean total edep per event
     assert np.isclose(mean.sum() * 1, (90.0 + 30.0) / 2)  # 2 events

tests/test_bump_dataset_version.py

@@ -197,7 +197,7 @@ def test_update_manifest_reports_missing_targets(tmp_path):
     # schema2 dir exists but the parquet file does not
     (tmp_path / "processed" / "steps" / "gen1" / "schema2").mkdir(parents=True)
 
-    lines, missing = plan_update_manifest(manifest, "schema2")
+    _, missing = plan_update_manifest(manifest, "schema2")
     assert len(missing) == 1
     assert "schema2" in str(missing[0])
 
@@ -313,7 +313,7 @@ def test_create_manifest_writes_relative_paths(tmp_path):
 def test_create_manifest_reports_missing_files(tmp_path):
     ghost = tmp_path / "processed" / "gen1" / "schema2" / "shard-000.parquet"
     output = tmp_path / "pools" / "full.manifest"
-    lines, missing, _ = plan_create_manifest(output, [ghost])
+    _, missing, _ = plan_create_manifest(output, [ghost])
     assert len(missing) == 1
     assert missing[0] == ghost.resolve()
 

tests/test_cli_train_overrides.py

@@ -9,7 +9,7 @@ from pathlib import Path
 
 from typer.testing import CliRunner
 
-import giant.cli as cli
+from giant import cli
 
 runner = CliRunner()
 

tests/test_cond_layout.py

@@ -1,4 +1,5 @@
 import pytest
+
 from giant.cond_layout import AXIS_TYPES, CondLayout
 from giant.constants import COND_DIM, COND_DIM_BASE, MATERIAL_PHYS_DIM, PARTICLE_PHYS_DIM
 

tests/test_config.py

@@ -576,7 +576,7 @@ def test_save_config_round_trips_three_level_nesting(tmp_path):
 # default_out_dir_name
 # ---------------------------------------------------------------------------
 
-_NOW = datetime(2026, 7, 29, 14, 30)
+_NOW = datetime(2026, 7, 29, 14, 30)  # noqa: DTZ001 - naive, matching default_out_dir_name's naive datetime.now()
 
 
 def _cfg_with(**dotted_overrides):

tests/test_config_consumed_keys.py

@@ -90,7 +90,7 @@ def _collect_names(source: str, filename: str) -> set[str]:
             names.add(node.attr)
         elif isinstance(node, ast.Constant) and isinstance(node.value, str) and id(node) not in docstring_ids:
             names.add(node.value)
-        elif isinstance(node, ast.arg):
+        elif isinstance(node, ast.arg):  # noqa: SIM114 - kept separate so ty narrows node.arg to str, not str | None
             names.add(node.arg)
         elif isinstance(node, ast.keyword) and node.arg is not None:
             names.add(node.arg)

tests/test_dwarf.py

@@ -1,3 +1,4 @@
+from test_pipeline import _make_synthetic_steps
 from typer.testing import CliRunner
 
 from giant import cli as giant_cli
@@ -5,7 +6,6 @@ from giant.config import Conditioning
 from giant.data import setup_cache
 from giant.tools import dwarf
 from giant.tools.dwarf import app
-from test_pipeline import _make_synthetic_steps
 
 runner = CliRunner()
 

tests/test_flow.py

@@ -1,9 +1,10 @@
 import torch
+
 from giant.config import ConditioningAxisConfig
 from giant.constants import COND_DIM
 from giant.model.network import Stage1Model
 from giant.model.schedule import CosineSchedule, flow_matching_loss
-from giant.sample import sample_flow, sample_ddim
+from giant.sample import sample_ddim, sample_flow
 
 PARTICLE_CFG = ConditioningAxisConfig(type="physical", emb_dim=8, n_layers=1)
 MATERIAL_CFG = ConditioningAxisConfig(type="physical", emb_dim=8, n_layers=1)

tests/test_network.py

@@ -2,8 +2,9 @@ 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.constants import COND_DIM, CONT_SLOT_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM
 from giant.model.network import (
     HISTORY_REGISTRY,
     AttentionHistory,
@@ -1217,18 +1218,18 @@ def test_stage_classes_are_stagemodel_subclasses(cls):
 @pytest.mark.parametrize("cls", [Stage1Model, Stage2OneShot, Stage2Autoregressive])
 @pytest.mark.parametrize("generator", ["flow", "ddpm", "wgan"])
 def test_stagemodel_time_emb_matches_objective_needs_time(cls, generator):
-    kwargs = dict(
-        pdg_vocab=5,
-        mat_vocab=3,
-        particle_cfg=PARTICLE_CFG,
-        material_cfg=MATERIAL_CFG,
-        hidden_dim=_STAGE_HIDDEN_DIM,
-        n_res_blocks=_STAGE_N_BLOCKS,
-        cond_out_dim=_STAGE_COND_OUT_DIM,
-        generator=generator,
-        time_dim=8,
-        noise_dim=8,
-    )
+    kwargs = {
+        "pdg_vocab": 5,
+        "mat_vocab": 3,
+        "particle_cfg": PARTICLE_CFG,
+        "material_cfg": MATERIAL_CFG,
+        "hidden_dim": _STAGE_HIDDEN_DIM,
+        "n_res_blocks": _STAGE_N_BLOCKS,
+        "cond_out_dim": _STAGE_COND_OUT_DIM,
+        "generator": generator,
+        "time_dim": 8,
+        "noise_dim": 8,
+    }
     if cls is Stage1Model:
         kwargs["n_sec_head_k_max"] = 15
     else:

tests/test_phase2.py

@@ -20,7 +20,6 @@ from giant.model.schedule import (
 )
 from giant.sample import sample_secondaries
 
-
 # ── helpers ──────────────────────────────────────────────────────────────────
 
 
@@ -341,7 +340,7 @@ def test_encode_secondaries_energy_conservation():
 def test_encode_secondaries_stick_logits_match_naive_reference():
     """Cumsum-based remaining-budget computation must match a naive
     per-row, per-slot Python reference (no cumsum) within float tolerance."""
-    from giant.data.transforms import encode_secondaries, _EPS, _STICK_LOGIT_CLIP
+    from giant.data.transforms import _EPS, _STICK_LOGIT_CLIP, encode_secondaries
 
     rng = np.random.default_rng(11)
     N = 25
@@ -569,7 +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, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
 
     for i, k in enumerate(n_sec):
         if k == 0:
@@ -593,7 +592,7 @@ 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_small, _, _, _, _ = 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]

tests/test_render.py

@@ -9,8 +9,8 @@ import pytest
 
 pytest.importorskip("plotstyle")
 
-from giant.analysis import render as render_mod  # noqa: E402
-from giant.analysis.reduced import Reduced  # noqa: E402
+from giant.analysis import render as render_mod
+from giant.analysis.reduced import Reduced
 
 
 def _try_render(reduced: list[Reduced], out: Path):

tests/test_rollout.py

@@ -9,7 +9,7 @@ import pytest
 import torch
 
 from giant.config import ConditioningAxisConfig, ParticleTypeConfig
-from giant.constants import TERM_ESCAPED, TERM_MAX_STEPS, TERM_UNKNOWN_PDG, K_MAX
+from giant.constants import K_MAX, TERM_ESCAPED, TERM_MAX_STEPS, TERM_UNKNOWN_PDG
 from giant.data.loader import TopNMap
 from giant.data.transforms import Normalizer
 from giant.model.network import (
@@ -21,7 +21,7 @@ from giant.model.network import (
 from giant.rollout import L1DistCollector, make_seed_frontier, rollout
 
 pytest.importorskip("sklearn")
-from giant import geometry as g  # noqa: E402
+from giant import geometry as g
 
 PDG_MAP = {22: 0, 11: 1, -11: 2}
 MAT_MAP = {"G4_AIR": 0, "G4_PbWO4": 1}

tests/test_router.py

@@ -1,5 +1,7 @@
 """Tests for the mixture-of-experts routing prototype (giant/model/network.py)."""
 
+import itertools
+
 import pytest
 import torch
 
@@ -7,6 +9,7 @@ from giant.config import ConditioningAxisConfig
 from giant.constants import COND_DIM, K_MAX, SEC_DIM, X_DIM
 from giant.model.network import (
     BLOCK_REGISTRY,
+    ROUTER_REGISTRY,
     TRUNK_REGISTRY,
     AdaLNResBlock,
     ComposedRouter,
@@ -17,7 +20,6 @@ from giant.model.network import (
     NoneRouter,
     PdgRouter,
     ProcessRouter,
-    ROUTER_REGISTRY,
     ResBlock,
     RoutedTrunk,
     Stage1Model,
@@ -397,7 +399,7 @@ def test_energy_router_own_width_controls_own_coverage_independent_of_others():
             router.raw_width[0] = raw
             shares.append(router.gate(cond_cont, cond_cat)[0, 0].item())
 
-    assert all(a <= b + 1e-6 for a, b in zip(shares, shares[1:]))
+    assert all(a <= b + 1e-6 for a, b in itertools.pairwise(shares))
 
 
 def test_build_router_threads_learn_width_kwargs_through():
@@ -1010,9 +1012,7 @@ def test_build_models_routed_pair_composed_router_is_drop_in_for_sample_flow():
     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(
-        stage2, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2
-    )
+    sec_cont, _, sec_valid = sample_secondaries(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)
 
@@ -1261,9 +1261,7 @@ def test_build_models_routed_pair_is_drop_in_for_sample_flow():
     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(
-        stage2, cond_cont, cond_cat, stage1_norm, n_sec_pred, steps=2
-    )
+    sec_cont, _, sec_valid = sample_secondaries(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)
 

tests/test_sample.py

@@ -262,7 +262,7 @@ 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_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]]
 
@@ -281,7 +281,7 @@ def test_sample_secondaries_ar_stop_token_forced_stop_gives_zero_secondaries(n_s
     _force_stop_head_logit(decoder, 50.0)
     cond_cont, cond_cat = _cond(B)
     stage1_out = torch.randn(B, X_DIM)
-    sec_cont, sec_type, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, None, steps=2)
+    _, _, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, None, steps=2)
     assert sec_valid.shape == (B, k_max)
     assert not sec_valid.any()
 
@@ -296,7 +296,7 @@ def test_sample_secondaries_ar_stop_token_forced_never_stop_runs_to_k_max(n_sec_
     _force_stop_head_logit(decoder, -50.0)
     cond_cont, cond_cat = _cond(B)
     stage1_out = torch.randn(B, X_DIM)
-    sec_cont, sec_type, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, None, steps=2)
+    _, _, sec_valid = sample_secondaries_ar(decoder, cond_cont, cond_cat, stage1_out, None, steps=2)
     assert sec_valid.all()
 
 
@@ -412,7 +412,7 @@ def test_sample_secondaries_ar_full_length_ignores_n_sec_pred_zero_rows():
     cond_cont, cond_cat = _cond(B)
     stage1_out = torch.randn(B, X_DIM)
     n_sec_pred = torch.tensor([0, 0, 0])
-    sec_cont, sec_type, sec_valid = sample_secondaries_ar(
+    sec_cont, _, sec_valid = sample_secondaries_ar(
         decoder, cond_cont, cond_cat, stage1_out, n_sec_pred, steps=2, full_length=True
     )
     assert not sec_valid.any()

tests/test_train.py

@@ -12,6 +12,7 @@ import pytest
 import torch
 import torch.nn.functional as F
 
+from giant.checkpoint_io import load_for_inference
 from giant.config import ParticleTypeConfig
 from giant.constants import (
     COND_DIM,
@@ -21,7 +22,6 @@ from giant.constants import (
     SEC_SLOT_DIM,
     X_DIM,
 )
-from giant.checkpoint_io import load_for_inference
 from giant.data.dataset import StepBatch
 from giant.data.transforms import Normalizer
 from giant.model.network import Stage2Autoregressive, build_critics, build_models
@@ -36,7 +36,6 @@ from giant.training import (
     train,
 )
 from giant.training.metrics import _wandb_run_config
-from giant.training.trainers import _type_class_weight_vector
 from giant.training.stage2_inputs import (
     _ar_has_prev,
     _assemble_stage2_ar_inputs,
@@ -51,6 +50,7 @@ from giant.training.stage2_inputs import (
     _stop_target_and_mask,
     _type_repr,
 )
+from giant.training.trainers import _type_class_weight_vector
 
 PDG_VOCAB = 6
 MAT_VOCAB = 3
@@ -543,19 +543,18 @@ def test_train_raises_when_no_active_stage():
     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,
-            )
+    with tempfile.TemporaryDirectory() as tmp, 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():

tests/test_training_plots.py

@@ -11,8 +11,8 @@ import pytest
 
 pytest.importorskip("plotstyle")
 
-from giant.training import plots as plots_mod  # noqa: E402
-from giant.training.plots import MetricsTable, derive_metrics_dir, render_metrics  # noqa: E402
+from giant.training import plots as plots_mod
+from giant.training.plots import MetricsTable, derive_metrics_dir, render_metrics
 
 # --- fixtures ----------------------------------------------------------
 

tests/test_transforms.py

@@ -2,9 +2,13 @@ import warnings
 
 import numpy as np
 import pytest
+
 from giant.cond_layout import AXIS_TYPES, CondLayout
 from giant.constants import COND_DIM, COND_DIM_BASE, K_MAX
 from giant.data.transforms import (
+    Normalizer,
+    _vectorized_map_lookup,
+    _WelfordAccumulator,
     build_cond_features,
     build_features,
     encode_secondaries,
@@ -14,12 +18,9 @@ from giant.data.transforms import (
     inv_log_transform,
     local_frame_rotation,
     log_transform,
-    Normalizer,
     reconstruct_post_pos,
     sorted_membership,
     travel_direction,
-    _vectorized_map_lookup,
-    _WelfordAccumulator,
 )