Merge pull request 'Feature/wandb integration' (#18) from feature/wandb-integration into master

Reviewed-on: #18

README.md

@@ -96,7 +96,7 @@ dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl
 giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl
 ```
 
-`train`/`predict` accept a TOML config file (`--config`) and CLI overrides for hyperparameters; see `--help` on any command for the full option list. `giant rollout` seeds showers from the highest-energy entry step per event, then autoregressively steps the two-stage model to completion — pushing secondaries as new tracks and looking up `material`/`layer_id` from the oracle at each step. Tracks terminate on energy cutoff, per-track max steps, detector escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
+`train`/`predict` accept a TOML config file (`--config`) and CLI overrides for hyperparameters; see `--help` on any command for the full option list. `giant train --wandb` logs per-epoch metrics (the same ones written to `metrics.csv`) to Weights & Biases; requires `uv sync --extra wandb`. `giant rollout` seeds showers from the highest-energy entry step per event, then autoregressively steps the two-stage model to completion — pushing secondaries as new tracks and looking up `material`/`layer_id` from the oracle at each step. Tracks terminate on energy cutoff, per-track max steps, detector escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
 
 ## Validation
 

configs/router_energy_n10_embedding.toml

@@ -0,0 +1,23 @@
+[train]
+mode          = "flow"
+epochs        = 30
+lr            = 3e-4
+warmup_epochs = 3
+val_fraction  = 0.1
+num_workers   = 4
+
+[model]
+conditioning      = "embedding"
+dropout           = 0.0
+expert_hidden_dim = 128
+expert_n_blocks   = 4
+
+[model.router]
+enabled           = true
+type              = "energy"
+n_experts         = 10
+expert_hidden_dim = 128
+expert_n_blocks   = 4
+temperature       = 0.05
+lambda_balance    = 0.035
+learn_centers     = true

configs/router_energy_n10_physical.toml

@@ -0,0 +1,23 @@
+[train]
+mode          = "flow"
+epochs        = 30
+lr            = 3e-4
+warmup_epochs = 3
+val_fraction  = 0.1
+num_workers   = 4
+
+[model]
+conditioning      = "physical"
+dropout           = 0.0
+expert_hidden_dim = 128
+expert_n_blocks   = 4
+
+[model.router]
+enabled           = true
+type              = "energy"
+n_experts         = 10
+expert_hidden_dim = 128
+expert_n_blocks   = 4
+temperature       = 0.05
+lambda_balance    = 0.035
+learn_centers     = true

configs/wgan_h128_b4_physical.toml

@@ -0,0 +1,13 @@
+[train]
+mode          = "wgan"
+epochs        = 30
+lr            = 3e-4
+warmup_epochs = 3
+val_fraction  = 0.1
+num_workers   = 4
+
+[model]
+hidden_dim   = 128
+n_blocks     = 4
+dropout      = 0.0
+conditioning = "physical"

giant/cli.py

@@ -1,5 +1,5 @@
 from collections import Counter
-from datetime import date, datetime, timezone
+from datetime import datetime, timezone
 from enum import Enum
 import math
 from pathlib import Path
@@ -87,8 +87,9 @@ def _batch_size_estimate_dims(model_cfg: dict, training: bool) -> tuple[int, int
     """
     router_cfg = model_cfg.get("router")
     if router_cfg and router_cfg.get("enabled"):
-        hidden_dim = model_cfg.get("expert_hidden_dim", 128)
-        n_blocks = model_cfg.get("expert_n_blocks", 3)
+        hidden_dim, n_blocks = gconfig.resolve_expert_dims(
+            router_cfg, model_cfg["hidden_dim"], model_cfg["n_blocks"]
+        )
         if training:
             n_blocks *= _router_total_experts(router_cfg)
         return hidden_dim, n_blocks
@@ -387,7 +388,10 @@ def train(
     out: Annotated[
         Optional[Path],
         typer.Option(
-            "--out", "-o", help="Checkpoint dir (default: auto from hyperparams)"
+            "--out",
+            "-o",
+            help="Checkpoint dir (default: timestamped dir from hyperparams, "
+            "or the --resume checkpoint's own dir when resuming)",
         ),
     ] = None,
     device: Annotated[
@@ -399,6 +403,30 @@ def train(
         Optional[Path],
         typer.Option("--resume", "-r", help="Checkpoint .pt to resume training from"),
     ] = None,
+    wandb: Annotated[
+        Optional[bool],
+        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],
+        typer.Option("--wandb-project", help="W&B project name (default: giant)"),
+    ] = None,
+    wandb_run_name: Annotated[
+        Optional[str],
+        typer.Option("--wandb-run-name", help="W&B run name (default: out_dir name)"),
+    ] = None,
+    wandb_log_every: Annotated[
+        Optional[int],
+        typer.Option(
+            "--wandb-log-every",
+            help="Log batch-level loss/grad_norm/lr to W&B every N optimizer "
+            "steps (default: 50); per-epoch metrics always log in full",
+        ),
+    ] = None,
 ) -> None:
     """Train the GIANT surrogate model."""
     batch_size_auto = False
@@ -436,6 +464,10 @@ def train(
             "n_critic": n_critic,
             "gp_weight": gp_weight,
             "critic_lr": critic_lr,
+            "wandb": wandb,
+            "wandb_project": wandb_project,
+            "wandb_run_name": wandb_run_name,
+            "wandb_log_every": wandb_log_every,
         }.items()
         if v is not None
     }
@@ -484,16 +516,28 @@ def train(
             f"batch_size: {t['batch_size']} (auto-estimated from free GPU memory)"
         )
 
-    out_dir = out or Path(
-        f"checkpoints/{date.today().strftime('%Y%m%d')}"
-        f"_{t['mode']}"
-        f"_h{m['hidden_dim']}"
-        f"_b{m['n_blocks']}"
-        f"_e{m['emb_dim']}"
-        f"_c{m['conditioning']}"
-        f"_lr{t['lr']}"
-        f"_bs{t['batch_size']}"
-    )
+    if out is not None:
+        out_dir = out
+    elif resume is not None:
+        # Continue writing into the resumed checkpoint's own directory
+        # rather than recomputing a hyperparam-derived name — the latter
+        # would (a) collide with the original run's dir only by accident
+        # (same day, unchanged hyperparams) and now never collides at all
+        # since the fresh-run name below is timestamped to the second, and
+        # (b) silently start a fresh directory if a resumed run tweaks any
+        # hyperparam baked into the name (e.g. --lr for a fine-tune).
+        out_dir = resume.parent
+    else:
+        out_dir = Path(
+            f"checkpoints/{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+            f"_{t['mode']}"
+            f"_h{m['hidden_dim']}"
+            f"_b{m['n_blocks']}"
+            f"_e{m['emb_dim']}"
+            f"_c{m['conditioning']}"
+            f"_lr{t['lr']}"
+            f"_bs{t['batch_size']}"
+        )
 
     typer.echo(f"device:  {_device}")
     typer.echo(f"out_dir: {out_dir}")

giant/config.py

@@ -35,6 +35,19 @@ DEFAULT_CONFIG: dict = {
         "n_critic": 5,
         "gp_weight": 10.0,
         "critic_lr": 0.0,
+        # Weights & Biases per-epoch metric logging (opt-in; see giant.train).
+        # "" for wandb_run_name means "use the checkpoint out_dir name" — not
+        # None, since save_config's TOML writer has no null literal to
+        # round-trip.
+        "wandb": False,
+        "wandb_project": "giant",
+        "wandb_run_name": "",
+        # Batch-granularity metrics (loss/grad_norm/lr) are logged every N
+        # optimizer steps, not every batch — a single epoch can be tens of
+        # thousands of steps (see steps_per_epoch above), and logging every
+        # one of them would flood the run with points the UI has to downsample
+        # anyway. Per-epoch metrics (the metrics.csv row) always log in full.
+        "wandb_log_every": 50,
     },
     "model": {
         "hidden_dim": 256,
@@ -54,8 +67,13 @@ DEFAULT_CONFIG: dict = {
             "enabled": False,
             "type": "energy",  # selects the Router impl from ROUTER_REGISTRY
             "n_experts": 4,
-            "expert_hidden_dim": 128,
-            "expert_n_blocks": 3,
+            # 0 means "inherit model.hidden_dim/n_blocks" (see
+            # resolve_expert_dims below) — not a fixed 128/3, which silently
+            # ignored --hidden-dim/--n-blocks whenever routing was enabled.
+            # TOML has no null literal to round-trip (same pattern as
+            # critic_lr/wandb_run_name above), hence 0 rather than None.
+            "expert_hidden_dim": 0,
+            "expert_n_blocks": 0,
             "temperature": 0.5,  # energy/pdg-router kwarg
             "learn_centers": True,  # energy/pdg-router kwarg
             "lambda_balance": 0.0,  # optional load-balance aux loss weight
@@ -250,6 +268,22 @@ def merge_cli_overrides(
     return cfg
 
 
+def resolve_expert_dims(
+    router_cfg: dict, hidden_dim: int, n_blocks: int
+) -> tuple[int, int]:
+    """Resolve a router's expert hidden_dim/n_blocks, inheriting from the
+    monolith's when left at the 0 ("unset") sentinel.
+
+    Used by both `giant.pipeline` (to build the checkpoint's `model_config`)
+    and `giant.cli`'s batch-size auto-estimate, so `--hidden-dim`/`--n-blocks`
+    size the experts the same way in both places unless
+    `router.expert_hidden_dim`/`expert_n_blocks` are explicitly overridden.
+    """
+    expert_hidden_dim = router_cfg.get("expert_hidden_dim") or hidden_dim
+    expert_n_blocks = router_cfg.get("expert_n_blocks") or n_blocks
+    return expert_hidden_dim, expert_n_blocks
+
+
 def seed_everything(seed: int) -> None:
     random.seed(seed)
     np.random.seed(seed)

giant/model/network.py

@@ -566,6 +566,30 @@ class Router(nn.Module):
         """
         return torch.zeros((), device=cond_cont.device)
 
+    def gate_stats(
+        self, cond_cont: torch.Tensor, cond_cat: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Diagnostics for catching a router that fails to specialize.
+
+        Returns `(norm_entropy, importance)`:
+          - `norm_entropy`: scalar, the batch-mean of each row's gate entropy
+            divided by `log(n_experts)`, in [0, 1] and comparable across
+            routers with different `n_experts` (1.0 = uniform/collapsed
+            gating, 0.0 = fully hard routing).
+          - `importance`: (n_experts,) tensor, `gate(...).sum(dim=0)` — the
+            *unnormalized* per-expert weight mass for this batch. Callers
+            wanting a global utilization share across many batches must sum
+            this across batches first and normalize once at the end;
+            averaging per-batch shares instead would treat every batch as
+            equally important regardless of size and understate a
+            rarely-but-fully-used expert.
+        """
+        gate = self.gate(cond_cont, cond_cat)  # (B, n_experts)
+        row_entropy = -(gate * (gate + 1e-8).log()).sum(dim=-1)  # (B,)
+        norm_entropy = row_entropy.mean() / math.log(self.n_experts)
+        importance = gate.sum(dim=0)  # (n_experts,)
+        return norm_entropy, importance
+
 
 ROUTER_REGISTRY: dict[str, type[Router]] = {}
 
@@ -1130,8 +1154,10 @@ def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]:
         shared = dict(
             pdg_vocab=pdg_vocab,
             mat_vocab=mat_vocab,
-            expert_hidden_dim=model_config.get("expert_hidden_dim", 128),
-            expert_n_blocks=model_config.get("expert_n_blocks", 3),
+            expert_hidden_dim=model_config.get("expert_hidden_dim")
+            or model_config.get("hidden_dim", 128),
+            expert_n_blocks=model_config.get("expert_n_blocks")
+            or model_config.get("n_blocks", 3),
             emb_dim=model_config.get("emb_dim", EMB_DIM),
             dropout=model_config.get("dropout", 0.1),
             conditioning=model_config.get("conditioning", "embedding"),

giant/pipeline.py

@@ -176,6 +176,25 @@ def run_train_job(
     )
 
     emb_dim = m.get("emb_dim", EMB_DIM)
+    expert_hidden_dim, expert_n_blocks = config.resolve_expert_dims(
+        router_cfg, m["hidden_dim"], m["n_blocks"]
+    )
+    if router_cfg.get("enabled") and (expert_hidden_dim, expert_n_blocks) != (
+        m["hidden_dim"],
+        m["n_blocks"],
+    ):
+        # Only reachable via an explicit router.expert_hidden_dim/n_blocks
+        # override (the 0/"unset" sentinel always resolves to m["hidden_dim"]/
+        # ["n_blocks"] — see resolve_expert_dims), so this is never a false
+        # positive from inheritance, only a deliberate narrow/wide-experts
+        # config the checkpoint dir name (_h{hidden_dim}_b{n_blocks}) won't
+        # reflect.
+        echo(
+            f"  warning: experts are {expert_hidden_dim}x{expert_n_blocks}, "
+            f"different from model.hidden_dim/n_blocks ({m['hidden_dim']}x"
+            f"{m['n_blocks']}) — the checkpoint dir name reflects the latter, "
+            "not the experts actually being trained"
+        )
 
     model_config = {
         "pdg_vocab": len(pdg_map),
@@ -188,8 +207,8 @@ def run_train_job(
         "sec_slot_dim": SEC_SLOT_DIM,
         "conditioning": conditioning,
         "router": dict(router_cfg),
-        "expert_hidden_dim": router_cfg["expert_hidden_dim"],
-        "expert_n_blocks": router_cfg["expert_n_blocks"],
+        "expert_hidden_dim": expert_hidden_dim,
+        "expert_n_blocks": expert_n_blocks,
         # Read by `predict`/`rollout` (which never receive their own --mode
         # flag) to auto-detect which sampler a checkpoint needs.
         "mode": t["mode"],
@@ -259,4 +278,8 @@ def run_train_job(
         n_critic=t.get("n_critic", 5),
         gp_weight=t.get("gp_weight", 10.0),
         critic_lr=t.get("critic_lr") or None,
+        use_wandb=t.get("wandb", False),
+        wandb_project=t.get("wandb_project", "giant"),
+        wandb_run_name=t.get("wandb_run_name", ""),
+        wandb_log_every=t.get("wandb_log_every", 50),
     )

giant/train.py

@@ -32,6 +32,7 @@ _METRICS_FIELDS = [
     "train_loss_s2",
     "train_loss_balance",
     "train_loss_proc",
+    "train_nsec_acc",
     "d_loss",
     "g_loss",
     "wasserstein_estimate",
@@ -42,9 +43,24 @@ _METRICS_FIELDS = [
     "val_loss_s2",
     "val_loss_balance",
     "val_loss_proc",
+    "val_nsec_acc",
     "val_marginal_kl",
+    "router_s1_entropy",
+    "router_s1_util_min",
+    "router_s1_util_max",
+    "router_s1_util_std",
+    "router_s2_entropy",
+    "router_s2_util_min",
+    "router_s2_util_max",
+    "router_s2_util_std",
     "lr",
+    "critic_lr",
     "grad_norm",
+    "grad_norm_d",
+    "grad_norm_g",
+    "gpu_mem_mb",
+    "samples_per_sec",
+    "is_best",
     "epoch_time_s",
 ]
 
@@ -108,9 +124,15 @@ def _compute_losses(
     lambda_balance: float = 0.0,
     lambda_proc: float = 0.0,
 ) -> tuple[
-    torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
 ]:
-    """Compute (total_loss, L_s1, L_nsec, L_s2, L_balance, L_proc) for one batch."""
+    """Compute (total_loss, L_s1, L_nsec, L_s2, L_balance, L_proc, nsec_acc) for one batch."""
     cond_cont, cond_cat, x1_s1, n_sec, sec_cont, proc_idx = batch
     cond_cont = cond_cont.to(device)
     cond_cat = cond_cat.to(device)
@@ -129,6 +151,7 @@ def _compute_losses(
     # n_sec classification loss
     n_sec_logits = stage1_model.predict_n_sec(cond_cont, cond_cat)
     l_nsec = F.cross_entropy(n_sec_logits, n_sec)
+    nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean()
 
     # Stage-2 secondary flow loss
     # Use a noiseless Stage-1 target as context (detach to avoid back-prop
@@ -171,7 +194,7 @@ def _compute_losses(
         total = total + lambda_balance * l_balance
     if lambda_proc > 0:
         total = total + lambda_proc * l_proc
-    return total, l_s1, l_nsec, l_s2, l_balance, l_proc
+    return total, l_s1, l_nsec, l_s2, l_balance, l_proc, nsec_acc
 
 
 def _wgan_train_step(
@@ -263,7 +286,9 @@ def _wgan_train_step(
 
     # --- Generator (+ n_sec) step ---
     did_g_step = step_count % n_critic == 0
-    l_nsec = F.cross_entropy(generator.predict_n_sec(cond_cont, cond_cat), n_sec)
+    n_sec_logits = generator.predict_n_sec(cond_cont, cond_cat)
+    l_nsec = F.cross_entropy(n_sec_logits, n_sec)
+    nsec_acc = (n_sec_logits.argmax(dim=-1) == n_sec).float().mean()
     optimizer_g.zero_grad()
     if did_g_step:
         g1 = generator_loss(critic_fn1, fake1)
@@ -283,8 +308,11 @@ def _wgan_train_step(
         "wasserstein_estimate": wasserstein_estimate,
         "gp_loss": (gp1 + lambda_s2 * gp2).detach(),
         "l_nsec": l_nsec.detach(),
+        "nsec_acc": nsec_acc.detach(),
         "did_g_step": did_g_step,
         "grad_norm": grad_norm_d.item() + grad_norm_g.item(),
+        "grad_norm_d": grad_norm_d.item(),
+        "grad_norm_g": grad_norm_g.item(),
     }
 
 
@@ -320,10 +348,64 @@ def train(
     n_critic: int = 5,
     gp_weight: float = 10.0,
     critic_lr: float | None = None,
+    use_wandb: bool = False,
+    wandb_project: str = "giant",
+    wandb_run_name: str = "",
+    wandb_log_every: int = 50,
 ) -> None:
     out_dir = Path(out_dir)
     out_dir.mkdir(parents=True, exist_ok=True)
 
+    stage1_params = sum(p.numel() for p in stage1_model.parameters())
+    sec_decoder_params = sum(p.numel() for p in sec_decoder.parameters())
+    critic_params = (
+        sum(p.numel() for p in critic.parameters()) if critic is not None else 0
+    )
+    sec_critic_params = (
+        sum(p.numel() for p in sec_critic.parameters()) if sec_critic is not None else 0
+    )
+    total_params = (
+        stage1_params + sec_decoder_params + critic_params + sec_critic_params
+    )
+
+    wandb_run = None
+    if use_wandb:
+        try:
+            import wandb
+        except ImportError as exc:
+            raise RuntimeError(
+                "train.wandb = true (--wandb) requires the 'wandb' package — "
+                "install it via `uv sync --extra wandb`"
+            ) from exc
+        # `id` is derived from out_dir so resuming a run (--resume) reattaches
+        # to the same wandb run instead of starting a new one.
+        wandb_run = wandb.init(
+            project=wandb_project,
+            name=wandb_run_name or out_dir.name,
+            id=out_dir.name,
+            resume="allow",
+            config={
+                "mode": mode,
+                "epochs": epochs,
+                "lr": lr,
+                "warmup_epochs": warmup_epochs,
+                "weight_decay": weight_decay,
+                "ema_decay": ema_decay,
+                "lambda_nsec": lambda_nsec,
+                "lambda_s2": lambda_s2,
+                "lambda_balance": lambda_balance,
+                "lambda_proc": lambda_proc,
+                "n_critic": n_critic,
+                "gp_weight": gp_weight,
+                "model": model_config or {},
+                "stage1_params": stage1_params,
+                "sec_decoder_params": sec_decoder_params,
+                "critic_params": critic_params,
+                "sec_critic_params": sec_critic_params,
+                "total_params": total_params,
+            },
+        )
+
     stage1_model = stage1_model.to(device)
     sec_decoder = sec_decoder.to(device)
     if mode == "wgan":
@@ -333,6 +415,13 @@ def train(
         critic = critic.to(device)
         sec_critic = sec_critic.to(device)
 
+    # MoE routing trunk (RoutedDenoisingMLP/RoutedSecondaryDecoder) is
+    # optional and orthogonal to `mode` — both stages carry a `.router`
+    # when enabled. Each router is an independent instance (their
+    # `n_experts` need not match), used both for the batch-level gate
+    # entropy snapshot below and the val-level gate stats further down.
+    has_router = hasattr(stage1_model, "router") and hasattr(sec_decoder, "router")
+
     # Flow-matching/diffusion models sample noticeably better from an EMA of
     # the weights than from the raw SGD-noisy ones — buffers (e.g. the fixed
     # sinusoidal-embedding freqs, or non-learned router centers) never change
@@ -394,6 +483,7 @@ def train(
 
     start_epoch = 1
     best_val_loss = float("inf")
+    resumed_global_step = 0
     if resume_path is not None:
         ckpt = torch.load(resume_path, map_location=device, weights_only=False)
         stage1_model.load_state_dict(ckpt["model"])
@@ -417,6 +507,7 @@ def train(
         lr_sched.load_state_dict(ckpt["lr_sched"])
         start_epoch = ckpt.get("epoch", 0) + 1
         best_val_loss = ckpt.get("best_val_loss", float("inf"))
+        resumed_global_step = ckpt.get("global_step", 0)
 
         # optimizer/lr_sched.load_state_dict() above restore the checkpoint's
         # own base LR, which would otherwise silently override an explicit
@@ -446,10 +537,16 @@ def train(
 
     epoch_w = len(str(epochs))
     last_completed_epoch = start_epoch - 1
-    global_step = 0
+    # Restored from the checkpoint on --resume so wandb_run.log(..., step=...)
+    # keeps advancing monotonically instead of restarting at 0 mid-run (a
+    # reattached wandb run — see wandb.init(id=..., resume="allow") below —
+    # would otherwise silently drop every post-resume point).
+    global_step = resumed_global_step
     with _GracefulShutdown() as shutdown:
         for epoch in range(start_epoch, epochs + 1):
             epoch_start = time.monotonic()
+            if device.type == "cuda":
+                torch.cuda.reset_peak_memory_stats(device)
             stage1_model.train()
             sec_decoder.train()
             if mode == "wgan":
@@ -466,6 +563,9 @@ def train(
             train_g_sum = 0.0
             train_wasserstein_sum = 0.0
             train_gp_sum = 0.0
+            train_nsec_acc_sum = 0.0
+            train_grad_norm_d_sum = 0.0
+            train_grad_norm_g_sum = 0.0
             train_n = 0
             train_batches = 0
             grad_norm_sum = 0.0
@@ -503,7 +603,6 @@ def train(
                         lambda_nsec,
                         lambda_s2,
                     )
-                    global_step += 1
                     if stats["did_g_step"]:
                         lr_sched.step()
                         if ema_decay > 0:
@@ -523,18 +622,23 @@ def train(
                     train_g_sum += stats["g_loss"].item() * B
                     train_wasserstein_sum += stats["wasserstein_estimate"].item() * B
                     train_gp_sum += stats["gp_loss"].item() * B
+                    train_nsec_acc_sum += stats["nsec_acc"].item() * B
+                    train_grad_norm_d_sum += stats["grad_norm_d"] * B
+                    train_grad_norm_g_sum += stats["grad_norm_g"] * B
                 else:
-                    loss, l_s1, l_nsec, l_s2, l_balance, l_proc = _compute_losses(
-                        stage1_model,
-                        sec_decoder,
-                        batch,
-                        mode,
-                        ddpm_schedule,
-                        device,
-                        lambda_nsec,
-                        lambda_s2,
-                        lambda_balance,
-                        lambda_proc,
+                    loss, l_s1, l_nsec, l_s2, l_balance, l_proc, nsec_acc = (
+                        _compute_losses(
+                            stage1_model,
+                            sec_decoder,
+                            batch,
+                            mode,
+                            ddpm_schedule,
+                            device,
+                            lambda_nsec,
+                            lambda_s2,
+                            lambda_balance,
+                            lambda_proc,
+                        )
                     )
                     optimizer.zero_grad()
                     loss.backward()
@@ -557,6 +661,7 @@ def train(
                     train_s2_sum += l_s2.item() * B
                     train_balance_sum += l_balance.item() * B
                     train_proc_sum += l_proc.item() * B
+                    train_nsec_acc_sum += nsec_acc.item() * B
 
                 train_n += B
                 train_batches += 1
@@ -573,6 +678,49 @@ def train(
                     f"loss={ema_loss:.4f} gnorm={ema_grad_norm:.3f}", refresh=False
                 )
 
+                global_step += 1
+                if (
+                    wandb_run is not None
+                    and wandb_log_every > 0
+                    and global_step % wandb_log_every == 0
+                ):
+                    log_payload = {
+                        "batch/epoch": epoch,
+                        "batch/loss": batch_loss,
+                        "batch/loss_ema": ema_loss,
+                        "batch/grad_norm": batch_grad_norm,
+                        "batch/lr": optimizer.param_groups[0]["lr"],
+                        "batch/critic_lr": (
+                            optimizer_d.param_groups[0]["lr"]
+                            if optimizer_d is not None
+                            else 0.0
+                        ),
+                    }
+                    if has_router:
+                        # Cheap re-use of the batch already in hand — no
+                        # extra data loading, just a small forward through
+                        # each router's own gate function. Only entropy is
+                        # logged at this granularity (not per-expert
+                        # utilization): a single batch's importance sum is
+                        # too noisy as a "global share" estimate, whereas
+                        # the val-loop aggregate (below) sums over the
+                        # whole val set for that. Batch-level entropy alone
+                        # is still enough to see a router collapsing in
+                        # real time, mid-epoch, rather than only at the
+                        # next validation pass.
+                        with torch.no_grad():
+                            cond_cont_b = batch[0].to(device)
+                            cond_cat_b = batch[1].to(device)
+                            s1_entropy, _ = stage1_model.router.gate_stats(
+                                cond_cont_b, cond_cat_b
+                            )
+                            s2_entropy, _ = sec_decoder.router.gate_stats(
+                                cond_cont_b, cond_cat_b
+                            )
+                        log_payload["batch/router_s1_entropy"] = s1_entropy.item()
+                        log_payload["batch/router_s2_entropy"] = s2_entropy.item()
+                    wandb_run.log(log_payload, step=global_step)
+
                 if shutdown.requested:
                     break
             bar.close()
@@ -582,7 +730,13 @@ def train(
 
             train_loss = train_loss_sum / max(train_n, 1)
             train_grad_norm = grad_norm_sum / max(train_batches, 1)
+            train_nsec_acc = train_nsec_acc_sum / max(train_n, 1)
+            train_grad_norm_d = train_grad_norm_d_sum / max(train_n, 1)
+            train_grad_norm_g = train_grad_norm_g_sum / max(train_n, 1)
             current_lr = optimizer.param_groups[0]["lr"]
+            critic_lr_value = (
+                optimizer_d.param_groups[0]["lr"] if optimizer_d is not None else 0.0
+            )
 
             stage1_model.eval()
             sec_decoder.eval()
@@ -618,8 +772,12 @@ def train(
                 val_loss = val_marginal_kl
                 val_s1_sum = val_nsec_sum = val_s2_sum = val_balance_sum = (
                     val_proc_sum
-                ) = 0.0
+                ) = val_nsec_acc_sum = 0.0
                 val_n = 1
+                val_nsec_acc = 0.0
+                router_s1_entropy = router_s2_entropy = 0.0
+                router_s1_util_min = router_s1_util_max = router_s1_util_std = 0.0
+                router_s2_util_min = router_s2_util_max = router_s2_util_std = 0.0
             else:
                 val_loss_sum = 0.0
                 val_s1_sum = 0.0
@@ -627,22 +785,36 @@ def train(
                 val_s2_sum = 0.0
                 val_balance_sum = 0.0
                 val_proc_sum = 0.0
+                val_nsec_acc_sum = 0.0
                 val_n = 0
+                if has_router:
+                    n_experts_s1 = stage1_model.router.n_experts
+                    n_experts_s2 = sec_decoder.router.n_experts
+                    val_router_s1_entropy_sum = 0.0
+                    val_router_s2_entropy_sum = 0.0
+                    val_router_s1_importance_sum = torch.zeros(
+                        n_experts_s1, device=device
+                    )
+                    val_router_s2_importance_sum = torch.zeros(
+                        n_experts_s2, device=device
+                    )
                 with torch.no_grad():
                     for val_batch_idx, batch in enumerate(val_loader):
                         if max_val_batches > 0 and val_batch_idx >= max_val_batches:
                             break
-                        loss, l_s1, l_nsec, l_s2, l_balance, l_proc = _compute_losses(
-                            stage1_model,
-                            sec_decoder,
-                            batch,
-                            mode,
-                            ddpm_schedule,
-                            device,
-                            lambda_nsec,
-                            lambda_s2,
-                            lambda_balance,
-                            lambda_proc,
+                        loss, l_s1, l_nsec, l_s2, l_balance, l_proc, nsec_acc = (
+                            _compute_losses(
+                                stage1_model,
+                                sec_decoder,
+                                batch,
+                                mode,
+                                ddpm_schedule,
+                                device,
+                                lambda_nsec,
+                                lambda_s2,
+                                lambda_balance,
+                                lambda_proc,
+                            )
                         )
                         B = batch[0].size(0)
                         val_loss_sum += loss.item() * B
@@ -651,8 +823,47 @@ def train(
                         val_s2_sum += l_s2.item() * B
                         val_balance_sum += l_balance.item() * B
                         val_proc_sum += l_proc.item() * B
+                        val_nsec_acc_sum += nsec_acc.item() * B
+                        if has_router:
+                            cond_cont = batch[0].to(device)
+                            cond_cat = batch[1].to(device)
+                            s1_entropy, s1_importance = stage1_model.router.gate_stats(
+                                cond_cont, cond_cat
+                            )
+                            s2_entropy, s2_importance = sec_decoder.router.gate_stats(
+                                cond_cont, cond_cat
+                            )
+                            val_router_s1_entropy_sum += s1_entropy.item() * B
+                            val_router_s2_entropy_sum += s2_entropy.item() * B
+                            val_router_s1_importance_sum += s1_importance
+                            val_router_s2_importance_sum += s2_importance
                         val_n += B
                 val_loss = val_loss_sum / max(val_n, 1)
+                val_nsec_acc = val_nsec_acc_sum / max(val_n, 1)
+
+                if has_router:
+                    router_s1_entropy = val_router_s1_entropy_sum / max(val_n, 1)
+                    router_s2_entropy = val_router_s2_entropy_sum / max(val_n, 1)
+                    s1_util = val_router_s1_importance_sum / (
+                        val_router_s1_importance_sum.sum().clamp_min(1e-8)
+                    )
+                    s2_util = val_router_s2_importance_sum / (
+                        val_router_s2_importance_sum.sum().clamp_min(1e-8)
+                    )
+                    router_s1_util_min = s1_util.min().item()
+                    router_s1_util_max = s1_util.max().item()
+                    router_s1_util_std = (
+                        s1_util.std().item() if n_experts_s1 > 1 else 0.0
+                    )
+                    router_s2_util_min = s2_util.min().item()
+                    router_s2_util_max = s2_util.max().item()
+                    router_s2_util_std = (
+                        s2_util.std().item() if n_experts_s2 > 1 else 0.0
+                    )
+                else:
+                    router_s1_entropy = router_s2_entropy = 0.0
+                    router_s1_util_min = router_s1_util_max = router_s1_util_std = 0.0
+                    router_s2_util_min = router_s2_util_max = router_s2_util_std = 0.0
 
                 if validate_every > 0 and epoch % validate_every == 0:
                     print(f"[epoch {epoch}] marginal validation:")
@@ -668,6 +879,11 @@ def train(
                     val_marginal_kl = float(np.mean(marginal_result["kl_divergence"]))
 
             epoch_time = time.monotonic() - epoch_start
+            gpu_mem_mb = (
+                torch.cuda.max_memory_allocated(device) / (1024 * 1024)
+                if device.type == "cuda"
+                else 0.0
+            )
 
             is_best = val_loss < best_val_loss
             marker = "  [best]" if is_best else ""
@@ -685,32 +901,53 @@ def train(
                 f"  lr {current_lr:.2e}  gnorm {train_grad_norm:.3f}"
                 f"  {epoch_time:.1f}s{marker}"
             )
-            metrics_writer.writerow(
-                {
-                    "epoch": epoch,
-                    "train_loss": train_loss,
-                    "train_loss_s1": train_s1_sum / max(train_n, 1),
-                    "train_loss_nsec": train_nsec_sum / max(train_n, 1),
-                    "train_loss_s2": train_s2_sum / max(train_n, 1),
-                    "train_loss_balance": train_balance_sum / max(train_n, 1),
-                    "train_loss_proc": train_proc_sum / max(train_n, 1),
-                    "d_loss": train_d_sum / max(train_n, 1),
-                    "g_loss": train_g_sum / max(train_n, 1),
-                    "wasserstein_estimate": train_wasserstein_sum / max(train_n, 1),
-                    "gp_loss": train_gp_sum / max(train_n, 1),
-                    "val_loss": val_loss,
-                    "val_loss_s1": val_s1_sum / max(val_n, 1),
-                    "val_loss_nsec": val_nsec_sum / max(val_n, 1),
-                    "val_loss_s2": val_s2_sum / max(val_n, 1),
-                    "val_loss_balance": val_balance_sum / max(val_n, 1),
-                    "val_loss_proc": val_proc_sum / max(val_n, 1),
-                    "val_marginal_kl": val_marginal_kl,
-                    "lr": current_lr,
-                    "grad_norm": train_grad_norm,
-                    "epoch_time_s": epoch_time,
-                }
-            )
+            metrics_row = {
+                "epoch": epoch,
+                "train_loss": train_loss,
+                "train_loss_s1": train_s1_sum / max(train_n, 1),
+                "train_loss_nsec": train_nsec_sum / max(train_n, 1),
+                "train_loss_s2": train_s2_sum / max(train_n, 1),
+                "train_loss_balance": train_balance_sum / max(train_n, 1),
+                "train_loss_proc": train_proc_sum / max(train_n, 1),
+                "train_nsec_acc": train_nsec_acc,
+                "d_loss": train_d_sum / max(train_n, 1),
+                "g_loss": train_g_sum / max(train_n, 1),
+                "wasserstein_estimate": train_wasserstein_sum / max(train_n, 1),
+                "gp_loss": train_gp_sum / max(train_n, 1),
+                "val_loss": val_loss,
+                "val_loss_s1": val_s1_sum / max(val_n, 1),
+                "val_loss_nsec": val_nsec_sum / max(val_n, 1),
+                "val_loss_s2": val_s2_sum / max(val_n, 1),
+                "val_loss_balance": val_balance_sum / max(val_n, 1),
+                "val_loss_proc": val_proc_sum / max(val_n, 1),
+                "val_nsec_acc": val_nsec_acc,
+                "val_marginal_kl": val_marginal_kl,
+                "router_s1_entropy": router_s1_entropy,
+                "router_s1_util_min": router_s1_util_min,
+                "router_s1_util_max": router_s1_util_max,
+                "router_s1_util_std": router_s1_util_std,
+                "router_s2_entropy": router_s2_entropy,
+                "router_s2_util_min": router_s2_util_min,
+                "router_s2_util_max": router_s2_util_max,
+                "router_s2_util_std": router_s2_util_std,
+                "lr": current_lr,
+                "critic_lr": critic_lr_value,
+                "grad_norm": train_grad_norm,
+                "grad_norm_d": train_grad_norm_d,
+                "grad_norm_g": train_grad_norm_g,
+                "gpu_mem_mb": gpu_mem_mb,
+                "samples_per_sec": train_n / max(epoch_time, 1e-8),
+                "is_best": int(is_best),
+                "epoch_time_s": epoch_time,
+            }
+            metrics_writer.writerow(metrics_row)
             metrics_file.flush()
+            if wandb_run is not None:
+                # Shares the same monotonic step axis as the per-batch
+                # `batch/*` logs above (global_step) rather than `epoch`,
+                # since a wandb run's `step` argument across `log()` calls
+                # must never decrease.
+                wandb_run.log(metrics_row, step=global_step)
 
             ckpt: dict = {
                 "model": stage1_model.state_dict(),
@@ -719,6 +956,7 @@ def train(
                 "lr_sched": lr_sched.state_dict(),
                 "epoch": epoch,
                 "best_val_loss": best_val_loss,
+                "global_step": global_step,
             }
             if mode == "wgan":
                 assert (
@@ -756,6 +994,8 @@ def train(
                 break
 
     metrics_file.close()
+    if wandb_run is not None:
+        wandb_run.finish()
 
     if shutdown.requested:
         print(

giant/validate.py

@@ -215,6 +215,8 @@ def validate_marginals(
     print("-" * 68)
     for j, name in enumerate(_SEC_PHYS_NAMES):
         r, g = phys_real[:, j], phys_gen[:, j]
+        if len(r) == 0 or len(g) == 0:
+            continue
         print(
             f"{name:<20} {r.mean():>10.4f} {g.mean():>10.4f} "
             f"{r.std():>10.4f} {g.std():>10.4f} {phys_kl[j]:>14.4f}"

pyproject.toml

@@ -25,11 +25,14 @@ dev = [
     "pytest>=8,<10",
     "ruff>=0.15,<1",
     "ty>=0.0.50,<0.1",
-    "giant[convert,analysis,geometry]",
+    "giant[convert,analysis,geometry,wandb]",
 ]
 geometry = [
     "scikit-learn>=1.4,<2",
 ]
+wandb = [
+    "wandb>=0.16,<1",
+]
 convert = [
     "uproot>=5.3,<6",
     "awkward>=2.6,<3",

tests/test_config.py

@@ -115,3 +115,33 @@ def test_warn_if_checkpoint_config_mismatch_no_warning_when_hashes_match(
 
     gconfig.warn_if_checkpoint_config_mismatch(ckpt_path)
     assert capsys.readouterr().err == ""
+
+
+def test_resolve_expert_dims_default_config_inherits_hidden_dim_and_n_blocks():
+    # The unset sentinel (expert_hidden_dim/n_blocks == 0 in DEFAULT_CONFIG)
+    # is exactly the bug fixed by resolve_expert_dims: it must not silently
+    # fall back to some other hardcoded default, only to the monolith's own
+    # hidden_dim/n_blocks, so --hidden-dim/--n-blocks reach the experts too.
+    router_cfg = dict(gconfig.DEFAULT_CONFIG["model"]["router"])
+    assert router_cfg["expert_hidden_dim"] == 0
+    assert router_cfg["expert_n_blocks"] == 0
+
+    hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6)
+    assert (hidden_dim, n_blocks) == (512, 6)
+
+
+def test_resolve_expert_dims_missing_keys_also_inherit():
+    hidden_dim, n_blocks = gconfig.resolve_expert_dims({}, 512, 6)
+    assert (hidden_dim, n_blocks) == (512, 6)
+
+
+def test_resolve_expert_dims_explicit_override_wins():
+    router_cfg = {"expert_hidden_dim": 128, "expert_n_blocks": 3}
+    hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6)
+    assert (hidden_dim, n_blocks) == (128, 3)
+
+
+def test_resolve_expert_dims_partial_override_mixes_explicit_and_inherited():
+    router_cfg = {"expert_hidden_dim": 128, "expert_n_blocks": 0}
+    hidden_dim, n_blocks = gconfig.resolve_expert_dims(router_cfg, 512, 6)
+    assert (hidden_dim, n_blocks) == (128, 6)  # n_blocks inherited, hidden_dim not

uv.lock

@@ -36,6 +36,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -125,6 +134,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/44/a1/70ebfffd6c6edc6034a547838ee46287c65ed89f710592ddc39c76b4a5a8/awkward_cpp-53-cp314-cp314t-win_arm64.whl", hash = "sha256:1be0c1d87d9f4fdf94b767a061df849f1bb21579d302b2996fb101527fc80a97", size = 551257, upload-time = "2026-06-08T12:31:56.319Z" },
 ]
 
+[[package]]
+name = "certifi"
+version = "2026.7.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/c2/24167ea9858356b47a87a50d39908bfdb72ceeefe0041586e704e5376b3a/certifi-2026.7.22.tar.gz", hash = "sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55", size = 138112, upload-time = "2026-07-22T03:35:12.644Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983, upload-time = "2026-07-22T03:35:11.276Z" },
+]
+
 [[package]]
 name = "cffi"
 version = "2.0.0"
@@ -182,6 +200,79 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" },
 ]
 
+[[package]]
+name = "charset-normalizer"
+version = "3.4.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bd/2a/23f34ec9d04624958e137efdc394888716353190e75f25dd22c7a2c7a8aa/charset_normalizer-3.4.9.tar.gz", hash = "sha256:673611bbd43f0810bec0b0f028ddeaaa501190339cac411f347ac76917c3ae7b", size = 152439, upload-time = "2026-07-07T14:34:58.454Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/70/4a/ecbd131485c07fcdfad54e28946d513e3da22ef3b4bd854dcafae54ec739/charset_normalizer-3.4.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:45b0cc4e3556cd875e09102988d1ab8356c998b596c9fced84547c8138b487a0", size = 319300, upload-time = "2026-07-07T14:33:15.666Z" },
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