Implement Phase 1: full data pipeline, model, training, and config support

- Data pipeline: loader (parquet→numpy), transforms (log, local-frame
  Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split
- Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP
- Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022)
- Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic
- Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint
- Validation: per-dimension marginal summary (normalised space)
- CLI: TOML config support with CLI-overrides; hyperparam-encoded output
  directory; config.toml with git hash saved into each run's checkpoint dir
- 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -1,6 +1,6 @@
 # giant
 
-**G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate.
+**G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate — a play on *Geant4* and the step function being the computationally heaviest part of the simulation.
 
 Proof-of-concept surrogate model for the Geant4 step function. Given a pre-step particle state, the model samples a physically plausible post-step outcome — replacing the stochastic Geant4 physics engine with a trained conditional generative model.
 

configs/default.toml

@@ -0,0 +1,14 @@
+# git: c3bf3abebfe29a10fe42b9cbafbb3460ab78d243
+
+[train]
+mode         = "flow"
+epochs       = 100
+batch_size   = 4096
+lr           = 3e-4
+val_fraction = 0.1
+num_workers  = 4
+
+[model]
+hidden_dim = 256
+n_blocks   = 6
+emb_dim    = 16

giant/data/dataset.py

@@ -1 +1,46 @@
-# PyTorch Dataset wrapping the preprocessed steps arrays.
+from __future__ import annotations
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class StepsDataset(Dataset):
+    def __init__(
+        self,
+        cond_cont: np.ndarray,
+        cond_cat: np.ndarray,
+        target: np.ndarray,
+    ) -> None:
+        self.cond_cont = torch.from_numpy(cond_cont).float()
+        self.cond_cat = torch.from_numpy(cond_cat).long()
+        self.target = torch.from_numpy(target).float()
+
+    def __len__(self) -> int:
+        return len(self.target)
+
+    def __getitem__(self, idx):
+        return self.cond_cont[idx], self.cond_cat[idx], self.target[idx]
+
+
+def train_val_split(
+    data: dict,
+    cond_cont: np.ndarray,
+    cond_cat: np.ndarray,
+    target: np.ndarray,
+    val_fraction: float = 0.1,
+    seed: int = 42,
+) -> tuple[StepsDataset, StepsDataset]:
+    rng = np.random.default_rng(seed)
+    unique_events = np.unique(data["event_id"])
+    rng.shuffle(unique_events)
+    n_val = max(1, int(len(unique_events) * val_fraction))
+    val_events = set(unique_events[:n_val].tolist())
+
+    val_mask = np.array([e in val_events for e in data["event_id"]])
+    train_mask = ~val_mask
+
+    return (
+        StepsDataset(cond_cont[train_mask], cond_cat[train_mask], target[train_mask]),
+        StepsDataset(cond_cont[val_mask], cond_cat[val_mask], target[val_mask]),
+    )

giant/data/loader.py

@@ -1 +1,33 @@
-# Load steps parquet files into numpy arrays.
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+def load_steps(path: str | Path) -> dict[str, np.ndarray]:
+    df = pd.read_parquet(path)
+    return {
+        "event_id": df["event_id"].to_numpy(),
+        "pdg": df["pdg"].to_numpy(dtype=np.int32),
+        "pre_pos": df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32),
+        "pre_energy": df["pre_energy"].to_numpy(dtype=np.float32),
+        "pre_dir": df[["pre_dir_x", "pre_dir_y", "pre_dir_z"]].to_numpy(dtype=np.float32),
+        "material": df["material_id"].to_numpy(dtype=np.int32),
+        "layer_id": df["layer_id"].to_numpy(dtype=np.int32),
+        "n_sec": df["n_secondaries"].to_numpy(dtype=np.int32),
+        "step_length": df["step_length"].to_numpy(dtype=np.float32),
+        "delta_e": (df["pre_energy"] - df["post_energy"]).to_numpy(dtype=np.float32),
+        "edep": df["edep"].to_numpy(dtype=np.float32),
+        "post_dir": df[["post_dir_x", "post_dir_y", "post_dir_z"]].to_numpy(dtype=np.float32),
+    }
+
+
+def build_index_maps(
+    data: dict[str, np.ndarray],
+) -> tuple[dict[int, int], dict[int, int]]:
+    pdg_vals = sorted(int(v) for v in np.unique(data["pdg"]))
+    mat_vals = sorted(int(v) for v in np.unique(data["material"]))
+    return (
+        {v: i for i, v in enumerate(pdg_vals)},
+        {v: i for i, v in enumerate(mat_vals)},
+    )

giant/data/transforms.py

@@ -1 +1,110 @@
-# Log transforms, local-frame direction rotation, and per-dimension normaliser.
+import numpy as np
+
+_EPS = 1e-8
+
+
+def log_transform(x: np.ndarray, eps: float = _EPS) -> np.ndarray:
+    return np.log(np.asarray(x, dtype=np.float32) + eps)
+
+
+def inv_log_transform(y: np.ndarray, eps: float = _EPS) -> np.ndarray:
+    return np.exp(np.asarray(y, dtype=np.float32)) - eps
+
+
+def local_frame_rotation(pre_dir: np.ndarray, post_dir: np.ndarray) -> np.ndarray:
+    """Rotate post_dir into the local frame where pre_dir maps to ẑ (Rodrigues).
+
+    Preserves the angle between pre_dir and post_dir; the result has post_dir
+    expressed relative to a coordinate system in which the incoming particle
+    travels along +z.
+    """
+    z = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
+
+    cos_t = np.clip((pre_dir * z).sum(axis=1, keepdims=True), -1.0, 1.0)  # (N,1)
+    sin_t = np.sqrt(np.maximum(0.0, 1.0 - cos_t ** 2))  # (N,1)
+
+    axis = np.cross(pre_dir, z)  # (N,3); zero when pre_dir ∥ ẑ
+    axis_norm = np.linalg.norm(axis, axis=1, keepdims=True)  # (N,1)
+
+    # Replace zero-norm axes with x̂ (the Rodrigues terms that involve the axis
+    # are multiplied by sin_t≈0 and (1-cos_t)≈0, so the choice is irrelevant).
+    safe_norm = np.where(axis_norm < 1e-7, 1.0, axis_norm)
+    axis = np.where(axis_norm < 1e-7, np.array([[1.0, 0.0, 0.0]]), axis / safe_norm)
+
+    kxv = np.cross(axis, post_dir)  # (N,3)
+    kdv = (axis * post_dir).sum(axis=1, keepdims=True)  # (N,1)
+
+    return (post_dir * cos_t + kxv * sin_t + axis * kdv * (1.0 - cos_t)).astype(np.float32)
+
+
+class Normalizer:
+    def __init__(self) -> None:
+        self.mean: np.ndarray | None = None
+        self.std: np.ndarray | None = None
+
+    def fit(self, X: np.ndarray) -> "Normalizer":
+        self.mean = X.mean(axis=0).astype(np.float32)
+        self.std = X.std(axis=0).astype(np.float32)
+        self.std = np.where(self.std < _EPS, 1.0, self.std).astype(np.float32)
+        return self
+
+    def transform(self, X: np.ndarray) -> np.ndarray:
+        return ((X - self.mean) / self.std).astype(np.float32)
+
+    def inverse_transform(self, X: np.ndarray) -> np.ndarray:
+        return (X * self.std + self.mean).astype(np.float32)
+
+    def to_dict(self) -> dict:
+        return {"mean": self.mean.tolist(), "std": self.std.tolist()}
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "Normalizer":
+        obj = cls()
+        obj.mean = np.array(d["mean"], dtype=np.float32)
+        obj.std = np.array(d["std"], dtype=np.float32)
+        return obj
+
+
+def build_features(
+    data: dict[str, np.ndarray],
+    pdg_map: dict[int, int],
+    mat_map: dict[int, int],
+    cond_normalizer: Normalizer | None = None,
+    target_normalizer: Normalizer | None = None,
+    fit: bool = False,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, Normalizer | None, Normalizer | None]:
+    """Assemble (cond_cont, cond_cat, target) arrays ready for StepsDataset.
+
+    When fit=True, new Normalizers are fitted on the supplied arrays.
+    """
+    post_dir_local = local_frame_rotation(data["pre_dir"], data["post_dir"])
+
+    target = np.column_stack([
+        log_transform(data["step_length"]),
+        log_transform(data["delta_e"]),
+        log_transform(data["edep"]),
+        post_dir_local,
+    ]).astype(np.float32)  # (N, 6)
+
+    cond_cont = np.column_stack([
+        data["pre_pos"],
+        log_transform(data["pre_energy"]),
+        data["pre_dir"],
+        data["layer_id"].astype(np.float32),
+        data["n_sec"].astype(np.float32),
+    ]).astype(np.float32)  # (N, 9)
+
+    pdg_idx = np.array([pdg_map[int(p)] for p in data["pdg"]], dtype=np.int64)
+    mat_idx = np.array([mat_map[int(m)] for m in data["material"]], dtype=np.int64)
+    cond_cat = np.column_stack([pdg_idx, mat_idx])  # (N, 2)
+
+    if fit:
+        cond_normalizer = Normalizer().fit(cond_cont)
+        target_normalizer = Normalizer().fit(target)
+
+    if cond_normalizer is not None:
+        cond_cont = cond_normalizer.transform(cond_cont)
+    if target_normalizer is not None:
+        target = target_normalizer.transform(target)
+
+    return cond_cont, cond_cat, target, cond_normalizer, target_normalizer

giant/model/network.py

@@ -1 +1,106 @@
-# SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP.
+import math
+
+import torch
+import torch.nn as nn
+
+
+class SinusoidalEmbedding(nn.Module):
+    def __init__(self, dim: int) -> None:
+        super().__init__()
+        assert dim % 2 == 0, "dim must be even"
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(10000) * torch.arange(half, dtype=torch.float32) / max(half - 1, 1)
+        )
+        self.register_buffer("freqs", freqs)
+
+    def forward(self, t: torch.Tensor) -> torch.Tensor:
+        t = t.reshape(-1, 1).float()
+        args = t * self.freqs.unsqueeze(0)  # (B, half)
+        return torch.cat([args.sin(), args.cos()], dim=-1)  # (B, dim)
+
+
+class ConditionEncoder(nn.Module):
+    def __init__(
+        self,
+        pdg_vocab: int,
+        mat_vocab: int,
+        cont_dim: int = 9,
+        emb_dim: int = 16,
+        out_dim: int = 128,
+    ) -> None:
+        super().__init__()
+        self.pdg_emb = nn.Embedding(pdg_vocab, emb_dim)
+        self.mat_emb = nn.Embedding(mat_vocab, emb_dim)
+        in_dim = cont_dim + 2 * emb_dim
+        self.mlp = nn.Sequential(
+            nn.Linear(in_dim, out_dim),
+            nn.SiLU(),
+            nn.Linear(out_dim, out_dim),
+        )
+
+    def forward(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor:
+        pdg_e = self.pdg_emb(cond_cat[:, 0])
+        mat_e = self.mat_emb(cond_cat[:, 1])
+        x = torch.cat([cond_cont, pdg_e, mat_e], dim=-1)
+        return self.mlp(x)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, dim: int, cond_dim: int) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.linear1 = nn.Linear(dim, dim)
+        self.cond_proj = nn.Linear(cond_dim, dim, bias=False)
+        self.act = nn.SiLU()
+        self.linear2 = nn.Linear(dim, dim)
+
+    def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
+        h = self.norm(x)
+        h = self.linear1(h) + self.cond_proj(cond)
+        h = self.act(h)
+        h = self.linear2(h)
+        return x + h
+
+
+class DenoisingMLP(nn.Module):
+    def __init__(
+        self,
+        pdg_vocab: int,
+        mat_vocab: int,
+        hidden_dim: int = 256,
+        n_blocks: int = 6,
+        emb_dim: int = 16,
+        time_dim: int = 64,
+        cond_out_dim: int = 128,
+        x_dim: int = 6,
+    ) -> None:
+        super().__init__()
+        self.time_emb = SinusoidalEmbedding(time_dim)
+        self.cond_enc = ConditionEncoder(
+            pdg_vocab=pdg_vocab,
+            mat_vocab=mat_vocab,
+            emb_dim=emb_dim,
+            out_dim=cond_out_dim,
+        )
+        merged_cond_dim = time_dim + cond_out_dim
+        self.input_proj = nn.Linear(x_dim, hidden_dim)
+        self.blocks = nn.ModuleList([
+            ResBlock(hidden_dim, merged_cond_dim) for _ in range(n_blocks)
+        ])
+        self.out_proj = nn.Linear(hidden_dim, x_dim)
+
+    def forward(
+        self,
+        x_t: torch.Tensor,
+        t: torch.Tensor,
+        cond_cont: torch.Tensor,
+        cond_cat: torch.Tensor,
+    ) -> torch.Tensor:
+        t_emb = self.time_emb(t)                          # (B, time_dim)
+        c_emb = self.cond_enc(cond_cont, cond_cat)        # (B, cond_out_dim)
+        cond = torch.cat([t_emb, c_emb], dim=-1)          # (B, time_dim+cond_out_dim)
+        x = self.input_proj(x_t)
+        for block in self.blocks:
+            x = block(x, cond)
+        return self.out_proj(x)

giant/model/schedule.py

@@ -1 +1,69 @@
-# CosineSchedule (DDPM forward process) and flow matching loss utilities.
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+class CosineSchedule:
+    """DDPM cosine noise schedule (Nichol & Dhariwal 2021)."""
+
+    def __init__(self, T: int = 1000, s: float = 0.008) -> None:
+        self.T = T
+        steps = np.arange(T + 1, dtype=np.float64)
+        f = np.cos(((steps / T + s) / (1.0 + s)) * np.pi / 2.0) ** 2
+        alpha_bars = (f / f[0]).astype(np.float32)
+        betas = np.clip(1.0 - alpha_bars[1:] / alpha_bars[:-1], 0.0, 0.999).astype(np.float32)
+
+        self.betas = torch.from_numpy(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":
+        self.betas = self.betas.to(device)
+        self.alphas = self.alphas.to(device)
+        self.alpha_bars = self.alpha_bars.to(device)
+        return self
+
+    def q_sample(
+        self,
+        x0: torch.Tensor,
+        t: torch.Tensor,
+        noise: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if noise is None:
+            noise = torch.randn_like(x0)
+        ab = self.alpha_bars[t].view(-1, 1)
+        return ab.sqrt() * x0 + (1.0 - ab).sqrt() * noise
+
+    def loss(
+        self,
+        model: torch.nn.Module,
+        x0: torch.Tensor,
+        cond_cont: torch.Tensor,
+        cond_cat: torch.Tensor,
+    ) -> torch.Tensor:
+        B = x0.size(0)
+        t = torch.randint(0, self.T, (B,), device=x0.device)
+        noise = torch.randn_like(x0)
+        x_t = self.q_sample(x0, t, noise)
+        t_norm = t.float() / self.T
+        pred = model(x_t, t_norm, cond_cont, cond_cat)
+        return F.mse_loss(pred, noise)
+
+
+def flow_matching_loss(
+    model: torch.nn.Module,
+    x1: torch.Tensor,
+    cond_cont: torch.Tensor,
+    cond_cat: torch.Tensor,
+) -> torch.Tensor:
+    """Conditional flow matching loss (Lipman et al. 2022).
+
+    Straight-line ODE path: x_t = (1-t)*x0 + t*x1, target field u_t = x1-x0.
+    """
+    B = x1.size(0)
+    t = torch.rand(B, device=x1.device)
+    x0 = torch.randn_like(x1)
+    x_t = (1.0 - t.view(-1, 1)) * x0 + t.view(-1, 1) * x1
+    u_t = x1 - x0
+    v_t = model(x_t, t, cond_cont, cond_cat)
+    return F.mse_loss(v_t, u_t)

giant/sample.py

@@ -1 +1,78 @@
-# DDPM, DDIM, and flow matching samplers.
+import torch
+
+
+@torch.no_grad()
+def sample_flow(
+    model: torch.nn.Module,
+    cond_cont: torch.Tensor,
+    cond_cat: torch.Tensor,
+    steps: int = 10,
+) -> torch.Tensor:
+    """Euler integration of the learned vector field from t=0 to t=1."""
+    model.eval()
+    B = cond_cont.size(0)
+    device = cond_cont.device
+    x = torch.randn(B, 6, device=device)
+    dt = 1.0 / steps
+    for i in range(steps):
+        t = torch.full((B,), i * dt, device=device)
+        v = model(x, t, cond_cont, cond_cat)
+        x = x + v * dt
+    return x
+
+
+@torch.no_grad()
+def sample_ddpm(
+    model: torch.nn.Module,
+    cond_cont: torch.Tensor,
+    cond_cat: torch.Tensor,
+    schedule,
+) -> torch.Tensor:
+    """Full DDPM ancestral sampling (T reverse steps)."""
+    model.eval()
+    B = cond_cont.size(0)
+    device = cond_cont.device
+    x = torch.randn(B, 6, device=device)
+    T = schedule.T
+    for i in reversed(range(T)):
+        t_norm = torch.full((B,), i / T, device=device)
+        eps_pred = model(x, t_norm, cond_cont, cond_cat)
+        beta = schedule.betas[i]
+        alpha = schedule.alphas[i]
+        alpha_bar = schedule.alpha_bars[i]
+        z = torch.randn_like(x) if i > 0 else torch.zeros_like(x)
+        x = (
+            (1.0 / alpha.sqrt())
+            * (x - (1.0 - alpha) / (1.0 - alpha_bar).sqrt() * eps_pred)
+            + beta.sqrt() * z
+        )
+    return x
+
+
+@torch.no_grad()
+def sample_ddim(
+    model: torch.nn.Module,
+    cond_cont: torch.Tensor,
+    cond_cat: torch.Tensor,
+    schedule,
+    steps: int = 50,
+) -> torch.Tensor:
+    """DDIM deterministic sampling (Song et al. 2020) with `steps` substeps."""
+    model.eval()
+    B = cond_cont.size(0)
+    device = cond_cont.device
+    T = schedule.T
+    timesteps = torch.linspace(T - 1, 0, steps, dtype=torch.long, device=device)
+    x = torch.randn(B, 6, device=device)
+    for step_idx, ts in enumerate(timesteps):
+        t_idx = int(ts.item())
+        t_norm = torch.full((B,), t_idx / T, device=device)
+        eps_pred = model(x, t_norm, cond_cont, cond_cat)
+        ab_t = schedule.alpha_bars[t_idx]
+        if step_idx + 1 < len(timesteps):
+            ab_prev = schedule.alpha_bars[int(timesteps[step_idx + 1].item())]
+        else:
+            ab_prev = torch.ones(1, device=device)
+        x0_pred = (x - (1.0 - ab_t).sqrt() * eps_pred) / ab_t.sqrt()
+        x = ab_prev.sqrt() * x0_pred + (1.0 - ab_prev).sqrt() * eps_pred
+    return x

giant/train.py

@@ -1 +1,82 @@
-# Training loop and validation loss evaluation.
+from pathlib import Path
+
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from giant.model.schedule import CosineSchedule, flow_matching_loss
+
+
+def train(
+    model: torch.nn.Module,
+    train_loader: DataLoader,
+    val_loader: DataLoader,
+    mode: str,
+    epochs: int,
+    lr: float,
+    device: torch.device,
+    out_dir: str | Path,
+    normalizer_dict: dict | None = None,
+    pdg_map: dict | None = None,
+    mat_map: dict | None = None,
+) -> None:
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    model = model.to(device)
+    optimizer = optim.AdamW(model.parameters(), lr=lr)
+    lr_sched = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
+
+    ddpm_schedule = CosineSchedule().to(device) if mode == "ddpm" else None
+    best_val_loss = float("inf")
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        train_loss = 0.0
+        for cond_cont, cond_cat, x1 in train_loader:
+            cond_cont = cond_cont.to(device)
+            cond_cat = cond_cat.to(device)
+            x1 = x1.to(device)
+
+            if mode == "flow":
+                loss = flow_matching_loss(model, x1, cond_cont, cond_cat)
+            else:
+                loss = ddpm_schedule.loss(model, x1, cond_cont, cond_cat)
+
+            optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step()
+            train_loss += loss.item() * x1.size(0)
+
+        train_loss /= len(train_loader.dataset)
+        lr_sched.step()
+
+        model.eval()
+        val_loss = 0.0
+        with torch.no_grad():
+            for cond_cont, cond_cat, x1 in val_loader:
+                cond_cont = cond_cont.to(device)
+                cond_cat = cond_cat.to(device)
+                x1 = x1.to(device)
+                if mode == "flow":
+                    loss = flow_matching_loss(model, x1, cond_cont, cond_cat)
+                else:
+                    loss = ddpm_schedule.loss(model, x1, cond_cont, cond_cat)
+                val_loss += loss.item() * x1.size(0)
+        val_loss /= len(val_loader.dataset)
+
+        print(f"epoch {epoch:4d}  train {train_loss:.4f}  val {val_loss:.4f}")
+
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            ckpt: dict = {"model": model.state_dict()}
+            if normalizer_dict is not None:
+                ckpt["normalizer"] = normalizer_dict
+            if pdg_map is not None:
+                ckpt["pdg_map"] = pdg_map
+            if mat_map is not None:
+                ckpt["mat_map"] = mat_map
+            torch.save(ckpt, out_dir / "best.pt")
+
+    torch.save({"model": model.state_dict()}, out_dir / "last.pt")

giant/validate.py

@@ -1 +1,60 @@
-# Step-level marginal comparisons and (later) shower-level rollout validation.
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from giant.sample import sample_flow, sample_ddpm, sample_ddim
+
+_TARGET_NAMES = [
+    "log_step_length",
+    "log_delta_e",
+    "log_edep",
+    "post_dir_x",
+    "post_dir_y",
+    "post_dir_z",
+]
+
+
+def validate_marginals(
+    model: torch.nn.Module,
+    val_loader: DataLoader,
+    mode: str = "flow",
+    schedule=None,
+    device: torch.device | None = None,
+    n_batches: int | None = None,
+) -> dict[str, np.ndarray]:
+    """Compare per-dimension marginals of generated vs. real steps.
+
+    Returns {"real": (N,6), "generated": (N,6)} in normalised space.
+    """
+    if device is None:
+        device = next(model.parameters()).device
+    model.eval()
+
+    all_real, all_gen = [], []
+    for i, (cond_cont, cond_cat, x1) in enumerate(val_loader):
+        if n_batches is not None and i >= n_batches:
+            break
+        cond_cont = cond_cont.to(device)
+        cond_cat = cond_cat.to(device)
+
+        if mode == "flow":
+            gen = sample_flow(model, cond_cont, cond_cat)
+        elif mode == "ddpm":
+            gen = sample_ddpm(model, cond_cont, cond_cat, schedule)
+        else:
+            gen = sample_ddim(model, cond_cont, cond_cat, schedule)
+
+        all_real.append(x1.numpy())
+        all_gen.append(gen.cpu().numpy())
+
+    real = np.concatenate(all_real, axis=0)
+    generated = np.concatenate(all_gen, axis=0)
+
+    header = f"{'Dim':<20} {'real_mean':>10} {'gen_mean':>10} {'real_std':>10} {'gen_std':>10}"
+    print(f"\n{header}")
+    print("-" * len(header))
+    for j, name in enumerate(_TARGET_NAMES):
+        r, g = real[:, j], generated[:, j]
+        print(f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} {r.std():>10.4f} {g.std():>10.4f}")
+
+    return {"real": real, "generated": generated}

scripts/train.py

@@ -1 +1,161 @@
-# CLI entry point: parse args, build dataset, instantiate model, call train loop.
+import argparse
+import subprocess
+import tomllib
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader
+
+from giant.data.loader import load_steps, build_index_maps
+from giant.data.transforms import build_features
+from giant.data.dataset import train_val_split
+from giant.model.network import DenoisingMLP
+from giant.train import train as run_training
+
+
+def _auto_device() -> torch.device:
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    if torch.backends.mps.is_available():
+        return torch.device("mps")
+    return torch.device("cpu")
+
+
+def _git_hash() -> str:
+    try:
+        return subprocess.check_output(
+            ["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL
+        ).decode().strip()
+    except Exception:
+        return "unknown"
+
+
+def _load_config(path: str) -> dict:
+    with open(path, "rb") as f:
+        return tomllib.load(f)
+
+
+def _save_config(cfg: dict, out_dir: Path) -> None:
+    lines = [f"# git: {_git_hash()}", ""]
+    for section, values in cfg.items():
+        lines.append(f"[{section}]")
+        for k, v in values.items():
+            lines.append(f"{k:<12} = {repr(v) if isinstance(v, str) else v}")
+        lines.append("")
+    (out_dir / "config.toml").write_text("\n".join(lines))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Train GIANT surrogate model")
+    parser.add_argument("--config", default=None, help="Path to TOML config file")
+    parser.add_argument("--data", required=True, help="Path to steps parquet file")
+    parser.add_argument("--mode", choices=["flow", "ddpm"])
+    parser.add_argument("--epochs", type=int)
+    parser.add_argument("--batch-size", type=int)
+    parser.add_argument("--lr", type=float)
+    parser.add_argument("--hidden-dim", type=int)
+    parser.add_argument("--n-blocks", type=int)
+    parser.add_argument("--emb-dim", type=int)
+    parser.add_argument("--val-fraction", type=float)
+    parser.add_argument("--out", default=None, help="Checkpoint output directory (default: auto from hyperparams)")
+    parser.add_argument("--device", default=None, help="cpu | cuda | mps (default: auto)")
+    parser.add_argument("--num-workers", type=int)
+    args = parser.parse_args()
+
+    # Defaults, overridden by config file, then by explicit CLI flags.
+    cfg: dict = {
+        "train": {
+            "mode": "flow", "epochs": 100, "batch_size": 4096, "lr": 3e-4,
+            "val_fraction": 0.1, "num_workers": 4,
+        },
+        "model": {
+            "hidden_dim": 256, "n_blocks": 6, "emb_dim": 16,
+        },
+    }
+
+    if args.config:
+        file_cfg = _load_config(args.config)
+        for section in ("train", "model"):
+            cfg[section].update(file_cfg.get(section, {}))
+
+    cli_train = {k: v for k, v in {
+        "mode": args.mode, "epochs": args.epochs, "batch_size": args.batch_size,
+        "lr": args.lr, "val_fraction": args.val_fraction, "num_workers": args.num_workers,
+    }.items() if v is not None}
+    cli_model = {k: v for k, v in {
+        "hidden_dim": args.hidden_dim, "n_blocks": args.n_blocks, "emb_dim": args.emb_dim,
+    }.items() if v is not None}
+    cfg["train"].update(cli_train)
+    cfg["model"].update(cli_model)
+
+    t, m = cfg["train"], cfg["model"]
+
+    device = torch.device(args.device) if args.device else _auto_device()
+
+    out_dir = Path(args.out or (
+        f"checkpoints/{t['mode']}"
+        f"_h{m['hidden_dim']}"
+        f"_b{m['n_blocks']}"
+        f"_e{m['emb_dim']}"
+        f"_lr{t['lr']}"
+        f"_bs{t['batch_size']}"
+    ))
+
+    print(f"device:  {device}")
+    print(f"out_dir: {out_dir}")
+
+    print("loading data …")
+    data = load_steps(args.data)
+    pdg_map, mat_map = build_index_maps(data)
+    print(f"  {len(data['event_id']):,} steps  |  {len(pdg_map)} PDG codes  |  {len(mat_map)} materials")
+
+    print("building features …")
+    cond_cont, cond_cat, target, cond_norm, tgt_norm = build_features(
+        data, pdg_map, mat_map, fit=True
+    )
+
+    train_ds, val_ds = train_val_split(
+        data, cond_cont, cond_cat, target, val_fraction=t["val_fraction"]
+    )
+    print(f"  train: {len(train_ds):,}  val: {len(val_ds):,}")
+
+    pin = device.type == "cuda"
+    train_loader = DataLoader(
+        train_ds, batch_size=t["batch_size"], shuffle=True,
+        num_workers=t["num_workers"], pin_memory=pin,
+    )
+    val_loader = DataLoader(
+        val_ds, batch_size=t["batch_size"], shuffle=False,
+        num_workers=t["num_workers"], pin_memory=pin,
+    )
+
+    model = DenoisingMLP(
+        pdg_vocab=len(pdg_map),
+        mat_vocab=len(mat_map),
+        hidden_dim=m["hidden_dim"],
+        n_blocks=m["n_blocks"],
+        emb_dim=m["emb_dim"],
+    )
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"model: {n_params:,} parameters")
+
+    out_dir.mkdir(parents=True, exist_ok=True)
+    _save_config(cfg, out_dir)
+
+    run_training(
+        model=model,
+        train_loader=train_loader,
+        val_loader=val_loader,
+        mode=t["mode"],
+        epochs=t["epochs"],
+        lr=t["lr"],
+        device=device,
+        out_dir=out_dir,
+        normalizer_dict={"cond": cond_norm.to_dict(), "target": tgt_norm.to_dict()},
+        pdg_map={str(k): v for k, v in pdg_map.items()},
+        mat_map={str(k): v for k, v in mat_map.items()},
+    )
+
+
+if __name__ == "__main__":
+    main()

tests/test_dataset.py

@@ -0,0 +1,62 @@
+import numpy as np
+import pytest
+from giant.data.dataset import StepsDataset, train_val_split
+
+
+def _dummy(N=500, n_events=20):
+    rng = np.random.default_rng(42)
+    data = {"event_id": rng.integers(0, n_events, size=N)}
+    cond_cont = rng.standard_normal((N, 9)).astype(np.float32)
+    cond_cat = rng.integers(0, 3, size=(N, 2)).astype(np.int64)
+    target = rng.standard_normal((N, 6)).astype(np.float32)
+    return data, cond_cont, cond_cat, target
+
+
+def test_dataset_length():
+    data, cond_cont, cond_cat, target = _dummy()
+    assert len(StepsDataset(cond_cont, cond_cat, target)) == len(target)
+
+
+def test_dataset_item_shapes():
+    data, cond_cont, cond_cat, target = _dummy()
+    c, k, t = StepsDataset(cond_cont, cond_cat, target)[0]
+    assert c.shape == (9,)
+    assert k.shape == (2,)
+    assert t.shape == (6,)
+
+
+def test_split_sizes_sum_to_total():
+    data, cond_cont, cond_cat, target = _dummy(N=500)
+    train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
+    assert len(train_ds) + len(val_ds) == 500
+
+
+def test_split_no_empty_sets():
+    data, cond_cont, cond_cat, target = _dummy(N=500, n_events=20)
+    train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
+    assert len(val_ds) > 0
+    assert len(train_ds) > 0
+
+
+def test_split_event_leakage():
+    """Train and val must not share any event_id."""
+    N = 1000
+    n_events = 50
+    rng = np.random.default_rng(7)
+    event_ids = rng.integers(0, n_events, size=N)
+    data = {"event_id": event_ids}
+    cond_cont = rng.standard_normal((N, 9)).astype(np.float32)
+    cond_cat = rng.integers(0, 3, size=(N, 2)).astype(np.int64)
+    target = rng.standard_normal((N, 6)).astype(np.float32)
+
+    train_ds, val_ds = train_val_split(data, cond_cont, cond_cat, target, val_fraction=0.2)
+
+    # Recover which event_ids ended up in each split via the indices
+    # (The dataset doesn't store event_ids, so we check via the original mask logic)
+    unique_events = np.unique(event_ids)
+    rng2 = np.random.default_rng(42)
+    rng2.shuffle(unique_events)
+    n_val = max(1, int(len(unique_events) * 0.2))
+    val_events = set(unique_events[:n_val].tolist())
+    train_events = set(unique_events[n_val:].tolist())
+    assert val_events.isdisjoint(train_events)

tests/test_flow.py

@@ -0,0 +1,59 @@
+import torch
+import pytest
+from giant.model.network import DenoisingMLP
+from giant.model.schedule import CosineSchedule, flow_matching_loss
+from giant.sample import sample_flow, sample_ddpm, sample_ddim
+
+
+def _small_model():
+    return DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
+
+
+def _batch(B=8):
+    x1 = torch.randn(B, 6)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.zeros(B, 2, dtype=torch.long)
+    return x1, cond_cont, cond_cat
+
+
+def test_flow_matching_loss_nonneg():
+    x1, cond_cont, cond_cat = _batch()
+    loss = flow_matching_loss(_small_model(), x1, cond_cont, cond_cat)
+    assert loss.item() >= 0.0
+
+
+def test_flow_matching_loss_is_scalar():
+    x1, cond_cont, cond_cat = _batch()
+    loss = flow_matching_loss(_small_model(), x1, cond_cont, cond_cat)
+    assert loss.shape == ()
+
+
+def test_flow_matching_loss_has_grad():
+    model = _small_model()
+    x1, cond_cont, cond_cat = _batch()
+    flow_matching_loss(model, x1, cond_cont, cond_cat).backward()
+    assert any(p.grad is not None for p in model.parameters())
+
+
+def test_sample_flow_shape():
+    B = 6
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.zeros(B, 2, dtype=torch.long)
+    out = sample_flow(_small_model(), cond_cont, cond_cat, steps=5)
+    assert out.shape == (B, 6)
+
+
+def test_ddpm_loss_nonneg():
+    schedule = CosineSchedule(T=50)
+    x1, cond_cont, cond_cat = _batch()
+    loss = schedule.loss(_small_model(), x1, cond_cont, cond_cat)
+    assert loss.item() >= 0.0
+
+
+def test_sample_ddim_shape():
+    B = 4
+    schedule = CosineSchedule(T=50)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.zeros(B, 2, dtype=torch.long)
+    out = sample_ddim(_small_model(), cond_cont, cond_cat, schedule, steps=5)
+    assert out.shape == (B, 6)

tests/test_network.py

@@ -0,0 +1,42 @@
+import torch
+import pytest
+from giant.model.network import DenoisingMLP, SinusoidalEmbedding
+
+
+def test_sinusoidal_embedding_shape():
+    emb = SinusoidalEmbedding(64)
+    t = torch.rand(16)
+    assert emb(t).shape == (16, 64)
+
+
+def test_sinusoidal_embedding_batch_1():
+    emb = SinusoidalEmbedding(32)
+    t = torch.tensor([0.5])
+    assert emb(t).shape == (1, 32)
+
+
+def test_denoising_mlp_output_shape():
+    B = 8
+    model = DenoisingMLP(pdg_vocab=5, mat_vocab=3)
+    x_t = torch.randn(B, 6)
+    t = torch.rand(B)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.stack([
+        torch.randint(0, 5, (B,)),
+        torch.randint(0, 3, (B,)),
+    ], dim=1)
+    out = model(x_t, t, cond_cont, cond_cat)
+    assert out.shape == (B, 6)
+
+
+def test_denoising_mlp_gradients_flow():
+    B = 4
+    model = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=32, n_blocks=2)
+    x_t = torch.randn(B, 6)
+    t = torch.rand(B)
+    cond_cont = torch.randn(B, 9)
+    cond_cat = torch.zeros(B, 2, dtype=torch.long)
+    loss = model(x_t, t, cond_cont, cond_cat).sum()
+    loss.backward()
+    for name, p in model.named_parameters():
+        assert p.grad is not None, f"no grad for {name}"

tests/test_transforms.py

@@ -0,0 +1,66 @@
+import numpy as np
+import pytest
+from giant.data.transforms import (
+    inv_log_transform,
+    local_frame_rotation,
+    log_transform,
+    Normalizer,
+)
+
+
+def test_log_transform_invertible():
+    x = np.array([0.1, 1.0, 10.0, 1000.0], dtype=np.float32)
+    np.testing.assert_allclose(inv_log_transform(log_transform(x)), x, rtol=1e-5)
+
+
+def test_local_frame_rotation_noop_when_aligned():
+    N = 8
+    pre_dir = np.tile([0.0, 0.0, 1.0], (N, 1)).astype(np.float32)
+    rng = np.random.default_rng(0)
+    post_dir = rng.standard_normal((N, 3)).astype(np.float32)
+    post_dir /= np.linalg.norm(post_dir, axis=1, keepdims=True)
+    result = local_frame_rotation(pre_dir, post_dir)
+    np.testing.assert_allclose(result, post_dir, atol=1e-5)
+
+
+def test_local_frame_rotation_preserves_angle():
+    """Angle between pre_dir and post_dir must equal angle between ẑ and rotated."""
+    rng = np.random.default_rng(1)
+    N = 200
+    pre_dir = rng.standard_normal((N, 3)).astype(np.float32)
+    pre_dir /= np.linalg.norm(pre_dir, axis=1, keepdims=True)
+    post_dir = rng.standard_normal((N, 3)).astype(np.float32)
+    post_dir /= np.linalg.norm(post_dir, axis=1, keepdims=True)
+
+    rotated = local_frame_rotation(pre_dir, post_dir)
+
+    cos_before = (pre_dir * post_dir).sum(axis=1)
+    cos_after = rotated[:, 2]  # dot with ẑ = z-component (unit vectors)
+    np.testing.assert_allclose(cos_after, cos_before, atol=1e-5)
+
+
+def test_local_frame_rotation_preserves_norm():
+    rng = np.random.default_rng(2)
+    N = 100
+    pre_dir = rng.standard_normal((N, 3)).astype(np.float32)
+    pre_dir /= np.linalg.norm(pre_dir, axis=1, keepdims=True)
+    post_dir = rng.standard_normal((N, 3)).astype(np.float32)
+    post_dir /= np.linalg.norm(post_dir, axis=1, keepdims=True)
+    result = local_frame_rotation(pre_dir, post_dir)
+    np.testing.assert_allclose(np.linalg.norm(result, axis=1), 1.0, atol=1e-5)
+
+
+def test_normalizer_roundtrip():
+    rng = np.random.default_rng(3)
+    X = rng.standard_normal((200, 9)).astype(np.float32)
+    norm = Normalizer().fit(X)
+    np.testing.assert_allclose(norm.inverse_transform(norm.transform(X)), X, atol=1e-5)
+
+
+def test_normalizer_serialization():
+    rng = np.random.default_rng(4)
+    X = rng.standard_normal((50, 6)).astype(np.float32)
+    norm = Normalizer().fit(X)
+    norm2 = Normalizer.from_dict(norm.to_dict())
+    np.testing.assert_allclose(norm2.mean, norm.mean)
+    np.testing.assert_allclose(norm2.std, norm.std)

uv.lock

@@ -0,0 +1,704 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
+]
+
+[[package]]
+name = "cuda-bindings"
+version = "13.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/67/5e7dba1ba576dd73da5dee894ca076ca5e959450dfff66d6d510a255d1f7/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7855c4868aabc0cfae28abbe83d56734bdfbd08f08fc234ac1912a12858bf49", size = 6025351, upload-time = "2026-05-29T23:11:49.685Z" },
+    { url = "https://files.pythonhosted.org/packages/39/2a/6d2e9047d1fb243dbaa364b01e0297534b9ed7fd27dba1c9f361519cf69b/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e32d08f71ebcdf00f0f41eab2eb37e8da94c8ed411cc9f7f7a019ce6b34abe3a", size = 6657965, upload-time = "2026-05-29T23:11:52.227Z" },
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