From c984d0a19d8390a56b558fb0bb953b48ea6f326a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 4 Sep 2026 14:09:29 +0200 Subject: [PATCH] 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 Claude-Session: https://claude.ai/code/session_01TMdZFqXXig7i3XkirSUxef --- giant/analysis/__init__.py | 22 +- giant/analysis/catalog.py | 4 +- giant/analysis/condor.py | 2 +- giant/analysis/context.py | 2 +- giant/analysis/grouping.py | 2 +- giant/analysis/reduced.py | 4 +- giant/analysis/render.py | 2 +- giant/analysis/router_gating.py | 12 +- giant/analysis/type_embedding_distance.py | 2 +- giant/cli.py | 254 +-- giant/config.py | 46 +- giant/data/loader.py | 3 +- giant/data/setup_cache.py | 8 +- giant/geometry.py | 11 +- giant/model/builders.py | 2 +- giant/model/encoders.py | 2 +- giant/model/history.py | 4 +- giant/model/layers.py | 2 +- giant/model/models.py | 2 +- giant/model/network.py | 10 +- giant/model/routers.py | 4 +- giant/model/schedule.py | 2 +- giant/model/summary.py | 4 +- giant/model/trunks.py | 2 +- giant/model/wgan.py | 2 +- giant/particles.py | 6 +- giant/pipeline.py | 8 +- giant/rollout.py | 69 +- giant/tools/bump_dataset_version.py | 4 +- giant/tools/check_migration_v02_v03.py | 6 +- giant/tools/create_root_files.py | 4 +- giant/tools/dwarf.py | 45 +- giant/tools/hparam_scan.py | 2 +- giant/tools/migrate_geant_steps.py | 2 +- giant/tools/steps_to_parquet_parallel.py | 2 +- giant/training/__init__.py | 2 +- giant/training/loop.py | 5 +- giant/training/metrics.py | 2 +- giant/training/plots.py | 2 +- giant/training/trainers.py | 4 +- tests/legacy/network_v02_snapshot.py | 22 +- tests/test_amp.py | 2 +- tests/test_analysis_reduce.py | 2 +- tests/test_bump_dataset_version.py | 4 +- tests/test_cli_train_overrides.py | 2 +- tests/test_cond_layout.py | 1 + tests/test_config.py | 2 +- tests/test_config_consumed_keys.py | 2 +- tests/test_dwarf.py | 2 +- tests/test_flow.py | 3 +- tests/test_network.py | 27 +- tests/test_phase2.py | 7 +- tests/test_render.py | 4 +- tests/test_rollout.py | 4 +- tests/test_router.py | 14 +- tests/test_sample.py | 8 +- tests/test_train.py | 29 +- tests/test_training_plots.py | 4 +- tests/test_transforms.py | 7 +- uv.lock | 2394 ++++++++++++--------- 60 files changed, 1686 insertions(+), 1427 deletions(-) diff --git a/giant/analysis/__init__.py b/giant/analysis/__init__.py index 1056d85..ff1623e 100644 --- a/giant/analysis/__init__.py +++ b/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", ] diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index f886edd..a3f009a 100644 --- a/giant/analysis/catalog.py +++ b/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 diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 8dbef2c..44058f2 100644 --- a/giant/analysis/condor.py +++ b/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())) diff --git a/giant/analysis/context.py b/giant/analysis/context.py index 2b273c2..655ce30 100644 --- a/giant/analysis/context.py +++ b/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"]) diff --git a/giant/analysis/grouping.py b/giant/analysis/grouping.py index bc42005..3db4447 100644 --- a/giant/analysis/grouping.py +++ b/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: diff --git a/giant/analysis/reduced.py b/giant/analysis/reduced.py index f697cbc..d2fe7a8 100644 --- a/giant/analysis/reduced.py +++ b/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())) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index 102049d..ccc5439 100644 --- a/giant/analysis/render.py +++ b/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 diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py index e82de47..33c308c 100644 --- a/giant/analysis/router_gating.py +++ b/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. diff --git a/giant/analysis/type_embedding_distance.py b/giant/analysis/type_embedding_distance.py index 9ad4383..48ba76b 100644 --- a/giant/analysis/type_embedding_distance.py +++ b/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. diff --git a/giant/cli.py b/giant/cli.py index 65a4fbb..8b96568 100644 --- a/giant/cli.py +++ b/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", diff --git a/giant/config.py b/giant/config.py index 8661e09..c4ac850 100644 --- a/giant/config.py +++ b/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), diff --git a/giant/data/loader.py b/giant/data/loader.py index 7897b2e..697693f 100644 --- a/giant/data/loader.py +++ b/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 diff --git a/giant/data/setup_cache.py b/giant/data/setup_cache.py index 6147392..f4235a2 100644 --- a/giant/data/setup_cache.py +++ b/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 diff --git a/giant/geometry.py b/giant/geometry.py index 824ddf0..8767369 100644 --- a/giant/geometry.py +++ b/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), diff --git a/giant/model/builders.py b/giant/model/builders.py index 6c1a8b3..ead1ed8 100644 --- a/giant/model/builders.py +++ b/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 diff --git a/giant/model/encoders.py b/giant/model/encoders.py index c82ab75..71bc998 100644 --- a/giant/model/encoders.py +++ b/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 diff --git a/giant/model/history.py b/giant/model/history.py index 072bc2e..8dfacd1 100644 --- a/giant/model/history.py +++ b/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: diff --git a/giant/model/layers.py b/giant/model/layers.py index 32c8aa1..22fd628 100644 --- a/giant/model/layers.py +++ b/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): diff --git a/giant/model/models.py b/giant/model/models.py index 6b60906..5ea429e 100644 --- a/giant/model/models.py +++ b/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 diff --git a/giant/model/network.py b/giant/model/network.py index 41be980..38094af 100644 --- a/giant/model/network.py +++ b/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", diff --git a/giant/model/routers.py b/giant/model/routers.py index a9060d3..e463733 100644 --- a/giant/model/routers.py +++ b/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) diff --git a/giant/model/schedule.py b/giant/model/schedule.py index c63e018..a964cae 100644 --- a/giant/model/schedule.py +++ b/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": diff --git a/giant/model/summary.py b/giant/model/summary.py index a00f524..fd6ef70 100644 --- a/giant/model/summary.py +++ b/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 diff --git a/giant/model/trunks.py b/giant/model/trunks.py index bdd5313..40c27c9 100644 --- a/giant/model/trunks.py +++ b/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 diff --git a/giant/model/wgan.py b/giant/model/wgan.py index a456266..393e193 100644 --- a/giant/model/wgan.py +++ b/giant/model/wgan.py @@ -1,4 +1,4 @@ -from typing import Callable +from collections.abc import Callable import torch diff --git a/giant/particles.py b/giant/particles.py index 657fd6b..7d083b5 100644 --- a/giant/particles.py +++ b/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, diff --git a/giant/pipeline.py b/giant/pipeline.py index f9c4618..51c8826 100644 --- a/giant/pipeline.py +++ b/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 diff --git a/giant/rollout.py b/giant/rollout.py index 36d5361..13b70ed 100644 --- a/giant/rollout.py +++ b/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. diff --git a/giant/tools/bump_dataset_version.py b/giant/tools/bump_dataset_version.py index 1e362c9..c5fd92e 100644 --- a/giant/tools/bump_dataset_version.py +++ b/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) diff --git a/giant/tools/check_migration_v02_v03.py b/giant/tools/check_migration_v02_v03.py index dd36f8a..ca0f6b5 100644 --- a/giant/tools/check_migration_v02_v03.py +++ b/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): diff --git a/giant/tools/create_root_files.py b/giant/tools/create_root_files.py index 4e54a2a..ea1bf33 100644 --- a/giant/tools/create_root_files.py +++ b/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( diff --git a/giant/tools/dwarf.py b/giant/tools/dwarf.py index 8840021..915e834 100644 --- a/giant/tools/dwarf.py +++ b/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[ diff --git a/giant/tools/hparam_scan.py b/giant/tools/hparam_scan.py index 0c64f6c..0e19a72 100644 --- a/giant/tools/hparam_scan.py +++ b/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 " diff --git a/giant/tools/migrate_geant_steps.py b/giant/tools/migrate_geant_steps.py index 447e0dc..2b9979f 100644 --- a/giant/tools/migrate_geant_steps.py +++ b/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]}, diff --git a/giant/tools/steps_to_parquet_parallel.py b/giant/tools/steps_to_parquet_parallel.py index 10794cd..2a9c1c7 100644 --- a/giant/tools/steps_to_parquet_parallel.py +++ b/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 diff --git a/giant/training/__init__.py b/giant/training/__init__.py index 5b9cb83..05375ea 100644 --- a/giant/training/__init__.py +++ b/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, diff --git a/giant/training/loop.py b/giant/training/loop.py index cfca43e..fb143e9 100644 --- a/giant/training/loop.py +++ b/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) diff --git a/giant/training/metrics.py b/giant/training/metrics.py index d1fb0dd..2453c73 100644 --- a/giant/training/metrics.py +++ b/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() diff --git a/giant/training/plots.py b/giant/training/plots.py index b15ef17..0afe38d 100644 --- a/giant/training/plots.py +++ b/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] diff --git a/giant/training/trainers.py b/giant/training/trainers.py index 9f94e5d..583a6c9 100644 --- a/giant/training/trainers.py +++ b/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 diff --git a/tests/legacy/network_v02_snapshot.py b/tests/legacy/network_v02_snapshot.py index a58992d..5810e9e 100644 --- a/tests/legacy/network_v02_snapshot.py +++ b/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), diff --git a/tests/test_amp.py b/tests/test_amp.py index e53ce12..a1ff50c 100644 --- a/tests/test_amp.py +++ b/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 diff --git a/tests/test_analysis_reduce.py b/tests/test_analysis_reduce.py index 0a456f0..befe649 100644 --- a/tests/test_analysis_reduce.py +++ b/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 diff --git a/tests/test_bump_dataset_version.py b/tests/test_bump_dataset_version.py index 6b48bf7..d9eca6e 100644 --- a/tests/test_bump_dataset_version.py +++ b/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() diff --git a/tests/test_cli_train_overrides.py b/tests/test_cli_train_overrides.py index 6cec1c2..7c38e68 100644 --- a/tests/test_cli_train_overrides.py +++ b/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() diff --git a/tests/test_cond_layout.py b/tests/test_cond_layout.py index f3bd1fb..818a0e3 100644 --- a/tests/test_cond_layout.py +++ b/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 diff --git a/tests/test_config.py b/tests/test_config.py index acc8fe1..132bbea 100644 --- a/tests/test_config.py +++ b/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): diff --git a/tests/test_config_consumed_keys.py b/tests/test_config_consumed_keys.py index 84d1a76..2c14b56 100644 --- a/tests/test_config_consumed_keys.py +++ b/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) diff --git a/tests/test_dwarf.py b/tests/test_dwarf.py index 3b7d899..82e7ef4 100644 --- a/tests/test_dwarf.py +++ b/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() diff --git a/tests/test_flow.py b/tests/test_flow.py index 6b0e256..59da560 100644 --- a/tests/test_flow.py +++ b/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) diff --git a/tests/test_network.py b/tests/test_network.py index 384a6b3..4843113 100644 --- a/tests/test_network.py +++ b/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: diff --git a/tests/test_phase2.py b/tests/test_phase2.py index 328262e..fbacbd8 100644 --- a/tests/test_phase2.py +++ b/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] diff --git a/tests/test_render.py b/tests/test_render.py index ec90a1e..589b45e 100644 --- a/tests/test_render.py +++ b/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): diff --git a/tests/test_rollout.py b/tests/test_rollout.py index 91f4113..c50c968 100644 --- a/tests/test_rollout.py +++ b/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} diff --git a/tests/test_router.py b/tests/test_router.py index b8057ce..00ffbd6 100644 --- a/tests/test_router.py +++ b/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) diff --git a/tests/test_sample.py b/tests/test_sample.py index 266a11f..b02195c 100644 --- a/tests/test_sample.py +++ b/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() diff --git a/tests/test_train.py b/tests/test_train.py index ffa3921..48de717 100644 --- a/tests/test_train.py +++ b/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(): diff --git a/tests/test_training_plots.py b/tests/test_training_plots.py index b095e70..2e52c5c 100644 --- a/tests/test_training_plots.py +++ b/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 ---------------------------------------------------------- diff --git a/tests/test_transforms.py b/tests/test_transforms.py index b1dfb43..cac0793 100644 --- a/tests/test_transforms.py +++ b/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 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