Unify FFN and TrajMixer model architectures

MODEL_ARCHITECTURES.md

@@ -0,0 +1,64 @@
+# Model architectures
+
+DeepHealth uses one codebase for both supported history-block architectures.
+Select the architecture explicitly when starting a training run:
+
+| `model_architecture` | History block | Checkpoint fingerprint |
+| --- | --- | --- |
+| `transformer_ffn_v1` | Temporal attention + SwiGLU FFN | `blocks.*.mlp.w1/w2/w3` and `blocks.*.ln2` |
+| `traj_mixer_v5` | Temporal attention + TrajMixer | `blocks.*.mlp.intra_*`, `gate_proj`, and `output_proj` |
+
+`transformer_ffn_v1` is the CLI default; pass `traj_mixer_v5` explicitly for
+TrajMixer runs.
+
+## Training
+
+Next-step example:
+
+```powershell
+python train_next_step.py --model_architecture traj_mixer_v5 --n_layer 12
+```
+
+All-future example:
+
+```powershell
+python train_all_future.py --model_architecture transformer_ffn_v1 --n_layer 12
+```
+
+New runs are separated by architecture:
+
+```text
+runs/
+  transformer_ffn_v1/
+    <run_name>/
+  traj_mixer_v5/
+    <run_name>/
+```
+
+Use `--runs_root` to place this structure under a different root. Existing run
+directories are not moved or renamed.
+
+Each generated `train_config.json` records `model_architecture`, total parameter
+count, and trainable parameter count.
+
+Both training entry points use the single `--n_layer` option to set the number
+of history backbone blocks. The same value is passed to `DeepHealth.n_layer`
+and saved as `n_layer` in `train_config.json`; it must be at least 1.
+
+## Architecture validation
+
+Evaluation resolves the architecture before constructing the model and always
+loads weights with `strict=True`.
+
+- Every config must include an explicit `model_architecture` marker.
+- Checkpoint fingerprints are used to validate that the selected architecture
+  matches the stored weights.
+- A config marker that conflicts with the checkpoint fingerprint raises an
+  error instead of silently choosing one architecture.
+- Unsupported historical TrajMixer markers such as `traj_mixer_v2`,
+  `traj_mixer_v3`, and `traj_mixer_v4` are rejected.
+- Checkpoints and configs created before architecture markers were introduced
+  are intentionally unsupported.
+
+Project code should use the architecture factory rather than instantiate a
+history block directly.

backbones.py

@@ -4,6 +4,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from model_architectures import (
+    TRAJ_MIXER_ARCHITECTURE,
+    TRANSFORMER_FFN_ARCHITECTURE,
+    resolve_model_architecture,
+)
+
 
 class TimeRoPE(nn.Module):
     def __init__(self, dim: int, base: float = 10000.0):
@@ -111,7 +117,10 @@ class TemporalAttention(nn.Module):
 
         # Layer-specific projection from shared RBF basis activations to per-head attention bias.
         self.rbf_proj = nn.Linear(n_rbf_bases, n_head, bias=False)
-        self.time_bias_scale = nn.Parameter(torch.tensor(0.0))
+        # Keep the initial RBF attention bias exactly zero through the
+        # zero-initialized projection, while leaving that projection with a
+        # live gradient from the first optimization step.
+        self.time_bias_scale = nn.Parameter(torch.tensor(1.0))
 
         self.resid_drop = nn.Dropout(dropout)
         self.reset_parameters()
@@ -210,7 +219,133 @@ class SwiGLU(nn.Module):
         return self.drop(self.w3(F.silu(self.w1(x)) * self.w2(x)))
 
 
-class GPTBlock(nn.Module):
+class TrajMixer(nn.Module):
+    """PreNorm gated mixing within and across latent trajectory groups.
+
+    The groups are contiguous partitions of the post-``W_O`` residual
+    representation. They are deliberately not treated as attention heads.
+    All operations are position-wise, so the sequence dimension remains fully
+    parallel and no temporal information can leak between positions here.
+    """
+
+    def __init__(
+        self,
+        n_embd: int,
+        n_head: int = 10,
+        dropout: float = 0.0,
+    ):
+        super().__init__()
+        if n_embd <= 0:
+            raise ValueError(f"n_embd must be > 0, got {n_embd}")
+        if n_head <= 0:
+            raise ValueError(f"n_head must be > 0, got {n_head}")
+        if n_embd % n_head != 0:
+            raise ValueError(
+                f"n_embd must be divisible by n_head, got {n_embd} and {n_head}"
+            )
+        self.n_embd = n_embd
+        self.n_group = n_head
+        self.d_group = n_embd // n_head
+        self.intra_hidden = 4 * self.d_group
+        self.hidden_group = 4 * n_head
+
+        self.norm = nn.LayerNorm(self.n_embd)
+
+        self.intra_gate_proj = nn.Parameter(
+            torch.empty(self.n_group, self.d_group, self.intra_hidden)
+        )
+        self.intra_value_proj = nn.Parameter(
+            torch.empty(self.n_group, self.d_group, self.intra_hidden)
+        )
+        self.intra_output_proj = nn.Parameter(
+            torch.empty(self.n_group, self.intra_hidden, self.d_group)
+        )
+        self.intra_gate_logits = nn.Parameter(
+            torch.empty(self.n_group, self.d_group)
+        )
+
+        self.gate_proj = nn.Parameter(
+            torch.empty(self.d_group, self.n_group, self.hidden_group)
+        )
+        self.value_proj = nn.Parameter(
+            torch.empty(self.d_group, self.n_group, self.hidden_group)
+        )
+        self.output_proj = nn.Parameter(
+            torch.empty(self.d_group, self.hidden_group, self.n_group)
+        )
+        self.drop = nn.Dropout(dropout)
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        for group_idx in range(self.n_group):
+            nn.init.xavier_uniform_(self.intra_gate_proj[group_idx])
+            nn.init.xavier_uniform_(self.intra_value_proj[group_idx])
+            nn.init.xavier_uniform_(self.intra_output_proj[group_idx])
+        nn.init.constant_(
+            self.intra_gate_logits,
+            math.log(0.1 / 0.9),
+        )
+
+        for feature_idx in range(self.d_group):
+            nn.init.xavier_uniform_(self.gate_proj[feature_idx])
+            nn.init.xavier_uniform_(self.value_proj[feature_idx])
+        nn.init.normal_(self.output_proj, mean=0.0, std=1e-3)
+
+    def _intra_mix(self, grouped: torch.Tensor) -> torch.Tensor:
+        """Mix features independently inside each residual-space group."""
+        intra_gate = torch.einsum(
+            "blgd,gdh->blgh", grouped, self.intra_gate_proj
+        )
+        intra_value = torch.einsum(
+            "blgd,gdh->blgh", grouped, self.intra_value_proj
+        )
+        intra_hidden = F.silu(intra_gate) * intra_value
+        return torch.einsum(
+            "blgh,ghd->blgd", intra_hidden, self.intra_output_proj
+        )
+
+    def _cross_mix(self, grouped: torch.Tensor) -> torch.Tensor:
+        """Mix groups independently for each within-group coordinate."""
+        gate = torch.einsum(
+            "blgr,rgh->blhr", grouped, self.gate_proj
+        )
+        value = torch.einsum(
+            "blgr,rgh->blhr", grouped, self.value_proj
+        )
+        hidden = F.silu(gate) * value
+        return torch.einsum(
+            "blhr,rhg->blgr", hidden, self.output_proj
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply one full-width PreNorm and one outer residual update."""
+        if x.ndim != 3:
+            raise ValueError(
+                f"TrajMixer expects a 3D tensor, got shape {tuple(x.shape)}"
+            )
+        if x.size(-1) != self.n_embd:
+            raise ValueError(
+                f"Expected hidden size {self.n_embd}, got {x.size(-1)}"
+            )
+
+        batch_size, seq_len, _ = x.shape
+        grouped = self.norm(x).reshape(
+            batch_size, seq_len, self.n_group, self.d_group
+        )
+
+        intra_output = self._intra_mix(grouped)
+        intra_gate = torch.sigmoid(self.intra_gate_logits).view(
+            1, 1, self.n_group, self.d_group
+        )
+        mixed_input = grouped + intra_gate * intra_output
+
+        update = self._cross_mix(mixed_input).reshape(
+            batch_size, seq_len, self.n_embd
+        )
+        return x + self.drop(update)
+
+
+class TransformerFFNBlock(nn.Module):
     def __init__(
         self,
         n_embd: int,
@@ -247,6 +382,75 @@ class GPTBlock(nn.Module):
         return x
 
 
+class TrajMixerBlock(nn.Module):
+    def __init__(
+        self,
+        n_embd: int,
+        n_head: int,
+        attn_dropout: float = 0.0,
+        mlp_dropout: float = 0.0,
+        use_time_rope: bool = False,
+        use_rbf_bias: bool = False,
+        n_rbf_bases: int = 16,
+    ):
+        super().__init__()
+        self.attn = TemporalAttention(
+            n_embd=n_embd,
+            n_head=n_head,
+            n_rbf_bases=n_rbf_bases,
+            dropout=attn_dropout,
+            use_time_rope=use_time_rope,
+            use_rbf_bias=use_rbf_bias,
+        )
+        self.mlp = TrajMixer(
+            n_embd=n_embd,
+            n_head=n_head,
+            dropout=mlp_dropout,
+        )
+        self.ln1 = nn.LayerNorm(n_embd)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        rope_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
+        rbf_cache: torch.Tensor | None = None,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        x = x + self.attn(self.ln1(x), rope_cache, rbf_cache, attn_mask)
+        return self.mlp(x)
+
+
+def build_backbone_block(
+    model_architecture: str,
+    *,
+    n_embd: int,
+    n_head: int,
+    attn_dropout: float = 0.0,
+    mlp_dropout: float = 0.0,
+    use_time_rope: bool = False,
+    use_rbf_bias: bool = False,
+    n_rbf_bases: int = 16,
+) -> nn.Module:
+    """Build one history block for a supported model architecture."""
+    architecture = resolve_model_architecture(model_architecture)
+    block_class: type[nn.Module]
+    if architecture == TRANSFORMER_FFN_ARCHITECTURE:
+        block_class = TransformerFFNBlock
+    elif architecture == TRAJ_MIXER_ARCHITECTURE:
+        block_class = TrajMixerBlock
+    else:  # pragma: no cover - guarded by resolve_model_architecture.
+        raise ValueError(f"Unsupported model architecture: {architecture!r}")
+    return block_class(
+        n_embd=n_embd,
+        n_head=n_head,
+        attn_dropout=attn_dropout,
+        mlp_dropout=mlp_dropout,
+        use_time_rope=use_time_rope,
+        use_rbf_bias=use_rbf_bias,
+        n_rbf_bases=n_rbf_bases,
+    )
+
+
 class TokenAutoDiscretization(nn.Module):
     def __init__(
         self,

delphi2m_auc_report.py

@@ -0,0 +1,183 @@
+"""Build Delphi2M-style sex-specific AUC reports.
+
+The Delphi2M evaluation code uses 0.1 years for the no-gap evaluation.  The
+published report displays that point as 0 months, while retaining the actual
+0.1-year evaluation period in this project's report output.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Dict, Optional
+
+import numpy as np
+import pandas as pd
+
+
+DEFAULT_DELPHI2M_PERIODS_YEARS = (0.1, 1.0, 5.0, 10.0)
+
+_CHAPTER_SHORT_NAMES = {
+    "I": "I. Infectious Diseases",
+    "II": "II. Neoplasms",
+    "III": "III. Blood & Immune Disorders",
+    "IV": "IV. Metabolic Diseases",
+    "V": "V. Mental Disorders",
+    "VI": "VI. Nervous System Diseases",
+    "VII": "VII. Eye Diseases",
+    "VIII": "VIII. Ear Diseases",
+    "IX": "IX. Circulatory Diseases",
+    "X": "X. Respiratory Diseases",
+    "XI": "XI. Digestive Diseases",
+    "XII": "XII. Skin Diseases",
+    "XIII": "XIII. Musculoskeletal Diseases",
+    "XIV": "XIV. Genitourinary Diseases",
+    "XV": "XV. Pregnancy & Childbirth",
+    "XVI": "XVI. Perinatal Conditions",
+    "XVII": "XVII. Congenital Abnormalities",
+    "XVIII": "XVIII. Symptoms & Signs",
+    "XIX": "XIX. Injury & Poisoning",
+    "XX": "XX. External Causes",
+    "XXI": "XXI. Health Services",
+    "XXII": "XXII. Special Purposes",
+    "Death": "Death",
+    "Unmapped": "Unmapped",
+}
+
+
+def _is_no_gap(period_years: float) -> bool:
+    return bool(np.isclose(float(period_years), 0.1, rtol=0.0, atol=1e-8))
+
+
+def _canonical_period_years(period_years: float) -> float:
+    value = float(period_years)
+    for canonical in DEFAULT_DELPHI2M_PERIODS_YEARS:
+        if np.isclose(value, canonical, rtol=0.0, atol=1e-6):
+            return float(canonical)
+    return value
+
+
+def _gap_months(period_years: float) -> int:
+    if _is_no_gap(period_years):
+        return 0
+    return int(round(float(period_years) * 12.0))
+
+
+def _gap_label(period_years: float) -> str:
+    if _is_no_gap(period_years):
+        return "No gap"
+    value = float(period_years)
+    value_text = f"{value:g}"
+    unit = "year" if np.isclose(value, 1.0) else "years"
+    return f"{value_text} {unit}"
+
+
+def _load_chapter_by_code(
+    chapter_mapping_path: Optional[str | Path] = None,
+) -> Dict[str, str]:
+    if chapter_mapping_path is None:
+        chapter_mapping_path = Path(__file__).with_name(
+            "icd10_chapter_organ_mapping.csv"
+        )
+    path = Path(chapter_mapping_path)
+    if not path.exists():
+        return {}
+
+    mapping = pd.read_csv(
+        path,
+        usecols=["code", "icd10_chapter"],
+        dtype={"code": str, "icd10_chapter": str},
+    )
+    mapping["code"] = mapping["code"].str.strip()
+    mapping["chapter"] = (
+        mapping["icd10_chapter"]
+        .str.strip()
+        .map(_CHAPTER_SHORT_NAMES)
+        .fillna("Unmapped")
+    )
+    return dict(zip(mapping["code"], mapping["chapter"]))
+
+
+def build_delphi2m_auc_report(
+    df_unpooled: pd.DataFrame,
+    *,
+    period_col: str,
+    chapter_mapping_path: Optional[str | Path] = None,
+) -> pd.DataFrame:
+    """Aggregate age strata by sex and return a Delphi2M-style AUC report.
+
+    Required input columns are ``token``, ``label_code``, ``sex``,
+    ``auc_delong``, and the supplied ``period_col`` (``offset`` or
+    ``horizon``).  The output begins with the five columns used by Delphi2M
+    Fig. 2e and then records the actual evaluation period and ICD-10 code.
+    """
+    required = {"token", "label_code", "sex", "auc_delong", period_col}
+    missing = sorted(required - set(df_unpooled.columns))
+    if missing:
+        raise ValueError(
+            "Cannot build Delphi2M AUC report; missing columns: "
+            + ", ".join(missing)
+        )
+
+    source = df_unpooled.loc[
+        :,
+        ["token", "label_code", "sex", "auc_delong", period_col],
+    ].copy()
+    source["sex"] = source["sex"].astype(str).str.strip().str.lower()
+    source = source[source["sex"].isin(["female", "male"])]
+    source["auc_delong"] = pd.to_numeric(
+        source["auc_delong"], errors="coerce"
+    )
+    source[period_col] = pd.to_numeric(source[period_col], errors="coerce")
+    source = source.dropna(subset=[period_col, "auc_delong"])
+    source[period_col] = source[period_col].map(_canonical_period_years)
+
+    if source.empty:
+        raise ValueError("Cannot build Delphi2M AUC report from empty AUC data.")
+
+    grouped = (
+        source.groupby(
+            ["token", "label_code", period_col, "sex"],
+            dropna=False,
+            as_index=False,
+        )
+        .agg(auc=("auc_delong", "mean"))
+    )
+    report = (
+        grouped.pivot(
+            index=["token", "label_code", period_col],
+            columns="sex",
+            values="auc",
+        )
+        .reset_index()
+        .rename_axis(columns=None)
+        .rename(columns={"female": "Female", "male": "Male"})
+    )
+    for col in ["Female", "Male"]:
+        if col not in report.columns:
+            report[col] = np.nan
+
+    chapter_by_code = _load_chapter_by_code(chapter_mapping_path)
+    report["chapter"] = (
+        report["label_code"].astype(str).map(chapter_by_code).fillna("Unmapped")
+    )
+    report["Gap, months"] = report[period_col].map(_gap_months).astype("Int64")
+    report["Gap label"] = report[period_col].map(_gap_label)
+    report["icd10"] = pd.to_numeric(report["token"], errors="coerce").astype(
+        "Int64"
+    )
+
+    report = report.sort_values(
+        ["icd10", period_col], kind="stable", ignore_index=True
+    )
+    return report.loc[
+        :,
+        [
+            "Gap, months",
+            "chapter",
+            "icd10",
+            "Female",
+            "Male",
+            period_col,
+            "Gap label",
+            "label_code",
+        ],
+    ]

evaluate_auc.py

@@ -7,7 +7,8 @@ This script follows the logic of the Delphi evaluation script supplied by the us
      at least `offset` years before the target time;
   3. run model inference by disease chunks to avoid materializing all logits;
   4. compute AUC separately by sex and age bracket;
-  5. aggregate age brackets with DeLong variance.
+  5. average age-bracket AUCs within each sex and write a Delphi2M-style
+     Female/Male report.
 
 Efficiency notes:
   - transformer/readout inference is executed once and cached;
@@ -39,7 +40,12 @@ from torch.utils.data import DataLoader, Subset
 from tqdm.auto import tqdm
 
 from dataset import HealthDataset
+from delphi2m_auc_report import (
+    DEFAULT_DELPHI2M_PERIODS_YEARS,
+    build_delphi2m_auc_report,
+)
 from eval_data import load_sequence_eval_dataset, sequence_eval_collate_fn
+from model_architectures import resolve_model_architecture
 from models import DeepHealth
 from readouts import build_readout
 from targets import PAD_IDX, CHECKUP_IDX, NO_EVENT_IDX
@@ -308,19 +314,24 @@ def split_indices(n: int, train_ratio: float, val_ratio: float, test_ratio: floa
     return idx[:n_train], idx[n_train:n_train + n_val], idx[n_train + n_val:]
 
 
-def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], dataset: HealthDataset) -> DeepHealth:
+def build_model_from_dataset(
+    args: argparse.Namespace,
+    cfg: Dict[str, Any],
+    dataset: HealthDataset,
+    state_dict: Optional[Dict[str, Any]] = None,
+) -> DeepHealth:
     model_target_mode = str(cfg_get(
         args, cfg, "model_target_mode", "next_token")).lower()
     if model_target_mode not in {"next_token", "all_future"}:
         raise ValueError(
             f"model_target_mode must be next_token or all_future, got {model_target_mode!r}"
         )
+    model_architecture = resolve_model_architecture(cfg, state_dict)
     return DeepHealth(
         vocab_size=dataset.vocab_size,
         n_embd=int(cfg_get(args, cfg, "n_embd", 120)),
         n_head=int(cfg_get(args, cfg, "n_head", 10)),
-        n_hist_layer=int(cfg_get(args, cfg, "n_hist_layer", 12)),
-        n_tab_layer=int(cfg_get(args, cfg, "n_tab_layer", 4)),
+        n_layer=int(cfg["n_layer"]),
         n_types=dataset.n_types,
         n_cont_types=dataset.n_cont_types,
         n_categories=dataset.n_categories,
@@ -331,6 +342,7 @@ def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], data
         time_mode=str(cfg_get(args, cfg, "time_mode", "relative")),
         dist_mode=str(cfg_get(args, cfg, "dist_mode", "exponential")),
         dropout=float(cfg_get(args, cfg, "dropout", 0.0)),
+        model_architecture=model_architecture,
     )
 
 
@@ -378,7 +390,7 @@ def resolve_dist_mode_for_checkpoint(cfg_dist_mode: str, state_dict: Dict[str, A
 
 
 def load_model_state(
-    model: torch.nn.Module,
+    model: DeepHealth,
     checkpoint_path: str,
     device: torch.device,
     state_dict: Optional[Dict[str, Any]] = None,
@@ -386,6 +398,7 @@ def load_model_state(
     state = state_dict if state_dict is not None else load_checkpoint_state_dict(
         checkpoint_path, map_location=device)
 
+    resolve_model_architecture(model.model_architecture, state)
     model.load_state_dict(state, strict=True)
 
 
@@ -1158,30 +1171,23 @@ def evaluate_auc_pipeline(
     df_auc_unpooled["label_code"] = df_auc_unpooled["token"].map(
         dataset.label_id_to_code)
 
-    print("Using DeLong method to calculate AUC confidence intervals.")
-    grouped = df_auc_unpooled.groupby(
-        ["token", "label_code", "offset"], dropna=False, as_index=False)
-    df_auc = grouped.agg(
-        auc=("auc_delong", "mean"),
-        n_strata=("auc_delong", "size"),
-        n_diseased=("n_diseased", "sum"),
-        n_healthy=("n_healthy", "sum"),
-        auc_variance_sum=("auc_variance_delong", "sum"),
+    print(
+        "Building Delphi2M-style report: mean AUC across age strata, "
+        "reported separately for Female and Male."
     )
-    df_auc["auc_variance_delong"] = (
-        df_auc["auc_variance_sum"]
-        / (df_auc["n_strata"].clip(lower=1).astype(np.float64) ** 2)
+    df_report = build_delphi2m_auc_report(
+        df_auc_unpooled,
+        period_col="offset",
     )
-    df_auc = df_auc.drop(columns=["auc_variance_sum"])
 
     if output_path is not None:
         out_dir = Path(output_path)
         out_dir.mkdir(parents=True, exist_ok=True)
-        df_auc.to_csv(out_dir / "df_both.csv", index=False)
-        df_auc_unpooled.to_csv(
-            out_dir / "df_auc_unpooled.csv", index=False)
+        report_path = out_dir / "df_auc_delphi2m_report.csv"
+        df_report.to_csv(report_path, index=False)
+        print(f"Saved Delphi2M-style AUC report: {report_path}")
 
-    return df_auc_unpooled, df_auc
+    return df_auc_unpooled, df_report
 
 
 # ---------------------------------------------------------------------------
@@ -1231,8 +1237,18 @@ def make_auc_offsets(args: argparse.Namespace, cfg: Dict[str, Any]) -> List[floa
     if explicit_offsets is not None:
         base_offsets = explicit_offsets
     else:
-        next_token_offset = float(cfg_get(args, cfg, "offset", 0.1))
-        base_offsets = [next_token_offset, 1.0, 5.0, 10.0]
+        next_token_offset = float(
+            cfg_get(
+                args,
+                cfg,
+                "offset",
+                DEFAULT_DELPHI2M_PERIODS_YEARS[0],
+            )
+        )
+        base_offsets = [
+            next_token_offset,
+            *DEFAULT_DELPHI2M_PERIODS_YEARS[1:],
+        ]
 
     offsets: List[float] = []
     seen = set()
@@ -1280,9 +1296,9 @@ def main() -> None:
     parser.add_argument("--filter_min_total", type=int, default=None,
                         help="Minimum metadata count for disease selection; default 0.")
     parser.add_argument("--offset", type=float, default=None,
-                        help="Next-token prediction offset in years; preserved and evaluated alongside 1, 5, and 10 years by default.")
+                        help="Next-token prediction offset in years; 0.1 is Delphi2M no gap and is evaluated alongside 1, 5, and 10 years by default.")
     parser.add_argument("--offsets", type=str, default=None,
-                        help="Comma-separated prediction offsets in years. Overrides the default set of offset,1,5,10.")
+                        help="Comma-separated prediction offsets in years. Overrides the default set of 0.1,1,5,10.")
     parser.add_argument("--age_start", type=float, default=None)
     parser.add_argument("--age_stop", type=float, default=None)
     parser.add_argument("--age_step", type=float, default=None)
@@ -1363,14 +1379,19 @@ def main() -> None:
     cfg = dict(cfg)
     cfg["dist_mode"] = dist_mode
     cfg["model_target_mode"] = model_target_mode
+    model_architecture = resolve_model_architecture(cfg, state_dict)
+    cfg["model_architecture"] = model_architecture
     print(f"Resolved dist_mode for evaluation: {dist_mode}")
+    print(f"Resolved model architecture: {model_architecture}")
     print(f"Model target mode for AUC: {model_target_mode}")
     print(
         "AUC score semantics: evaluate_auc.py uses disease-specific eta/logit scores; "
         "dist_mode affects model loading but is not converted to horizon-specific risk probability."
     )
 
-    model = build_model_from_dataset(args, cfg, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, str(model_ckpt_path),
                      device, state_dict=state_dict)
     model.eval()

evaluate_auc_v2.py

@@ -3,6 +3,9 @@
 This script supports DeepHealth fixed-horizon risk scores for exponential,
 Weibull, and mixed all-future distributions.
 
+The default horizons are 0.1, 1, 5, and 10 years.  As in Delphi2M, 0.1 years
+is reported as the no-gap evaluation.
+
 Landmark querying depends on the model target mode saved in train_config.json:
     - next_token: insert a <NO_EVENT> token at landmark age and read it out;
     - all_future: pass landmark age directly as t_query.
@@ -28,7 +31,12 @@ from torch.utils.data import DataLoader, Dataset
 from tqdm.auto import tqdm
 
 from dataset import HealthDataset
+from delphi2m_auc_report import (
+    DEFAULT_DELPHI2M_PERIODS_YEARS,
+    build_delphi2m_auc_report,
+)
 from eval_data import load_sequence_eval_dataset
+from model_architectures import resolve_model_architecture
 from models import DeepHealth
 from readouts import build_readout
 from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX
@@ -177,19 +185,24 @@ def resolve_dist_mode_for_checkpoint(cfg_dist_mode: str, state_dict: Dict[str, A
     return mode if mode in {"exponential", "weibull", "mixed"} else "exponential"
 
 
-def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], dataset: HealthDataset) -> DeepHealth:
+def build_model_from_dataset(
+    args: argparse.Namespace,
+    cfg: Dict[str, Any],
+    dataset: HealthDataset,
+    state_dict: Optional[Dict[str, Any]] = None,
+) -> DeepHealth:
     model_target_mode = str(cfg_get(
         args, cfg, "model_target_mode", "next_token")).lower()
     if model_target_mode not in {"next_token", "all_future"}:
         raise ValueError(
             f"model_target_mode must be next_token or all_future, got {model_target_mode!r}"
         )
+    model_architecture = resolve_model_architecture(cfg, state_dict)
     return DeepHealth(
         vocab_size=dataset.vocab_size,
         n_embd=int(cfg_get(args, cfg, "n_embd", 120)),
         n_head=int(cfg_get(args, cfg, "n_head", 10)),
-        n_hist_layer=int(cfg_get(args, cfg, "n_hist_layer", 12)),
-        n_tab_layer=int(cfg_get(args, cfg, "n_tab_layer", 4)),
+        n_layer=int(cfg["n_layer"]),
         n_types=dataset.n_types,
         n_cont_types=dataset.n_cont_types,
         n_categories=dataset.n_categories,
@@ -200,10 +213,12 @@ def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], data
         time_mode=str(cfg_get(args, cfg, "time_mode", "relative")),
         dist_mode=str(cfg_get(args, cfg, "dist_mode", "exponential")),
         dropout=float(cfg_get(args, cfg, "dropout", 0.0)),
+        model_architecture=model_architecture,
     )
 
 
-def load_model_state(model: torch.nn.Module, state_dict: Dict[str, Any]) -> None:
+def load_model_state(model: DeepHealth, state_dict: Dict[str, Any]) -> None:
+    resolve_model_architecture(model.model_architecture, state_dict)
     model.load_state_dict(state_dict, strict=True)
 
 
@@ -324,44 +339,6 @@ def _first_existing_column(df: pd.DataFrame, candidates: Sequence[str]) -> Optio
     return None
 
 
-def build_metadata_for_merge(dataset: HealthDataset, labels_meta: Optional[pd.DataFrame]) -> pd.DataFrame:
-    base_rows = []
-    for token, code in dataset.label_id_to_code.items():
-        token = int(token)
-        code_text = str(code)
-        if token in SPECIAL_TOKENS or code_text.startswith("<"):
-            continue
-        base_rows.append({"token": token, "label_code": code_text})
-    base = pd.DataFrame(base_rows)
-    if labels_meta is None or labels_meta.empty:
-        return base
-
-    meta = labels_meta.copy()
-    code_col = _first_existing_column(
-        meta, ["Name", "code", "ICD10", "icd10", "label", "token", "disease_code"])
-    if code_col is not None:
-        meta["_label_code"] = meta[code_col].astype(
-            str).map(lambda s: s.split()[0].strip())
-        merged = base.merge(meta, left_on="label_code",
-                            right_on="_label_code", how="left")
-        return merged.drop(columns=["_label_code"], errors="ignore")
-
-    if "index" in meta.columns:
-        idx = pd.to_numeric(meta["index"], errors="coerce")
-        has_no_event = (
-            NO_EVENT_IDX in dataset.label_id_to_code
-            and dataset.label_id_to_code.get(NO_EVENT_IDX) == "<NO_EVENT>"
-        )
-        if has_no_event:
-            idx = idx.where(idx < NO_EVENT_IDX, idx + 1)
-        meta["_index_int"] = idx.astype("Int64")
-        merged = base.merge(meta, left_on="token",
-                            right_on="_index_int", how="left")
-        return merged.drop(columns=["_index_int"], errors="ignore")
-
-    return base
-
-
 def _metadata_count_map(dataset: HealthDataset, labels_meta: Optional[pd.DataFrame]) -> Dict[int, float]:
     if labels_meta is None or labels_meta.empty or "count" not in labels_meta.columns:
         return {}
@@ -1101,7 +1078,6 @@ def evaluate_landmark_auc(
     loader: DataLoader,
     landmark_dataset: LandmarkDataset,
     output_path: Path,
-    labels_meta: Optional[pd.DataFrame],
     disease_ids: Sequence[int],
     disease_chunk_size: int,
     score_mode: str,
@@ -1118,7 +1094,6 @@ def evaluate_landmark_auc(
     use_amp: bool,
     hidden_cache_dtype: str,
     logit_batch_size: int,
-    meta_info: Dict[str, Any],
 ) -> Tuple[pd.DataFrame, pd.DataFrame]:
     model.eval().to(device)
 
@@ -1235,54 +1210,21 @@ def evaluate_landmark_auc(
     df_unpooled["label_code"] = df_unpooled["token"].map(
         landmark_dataset.dataset.label_id_to_code)
 
-    for k, v in meta_info.items():
-        df_unpooled[k] = v
-
-    meta_table = build_metadata_for_merge(landmark_dataset.dataset, labels_meta)
-    df_unpooled = df_unpooled.merge(
-        meta_table, on=["token", "label_code"], how="left")
-
-    grouped = df_unpooled.groupby(
-        ["token", "label_code", "horizon"], dropna=False, as_index=False)
-    df_merged = grouped.agg(
-        auc=("auc_delong", "mean"),
-        n_strata=("auc_delong", "size"),
-        n_diseased=("n_diseased", "sum"),
-        n_healthy=("n_healthy", "sum"),
-        auc_variance_sum=("auc_variance_delong", "sum"),
+    print(
+        "Building Delphi2M-style report: mean AUC across landmark-age "
+        "strata, reported separately for Female and Male."
     )
-    df_merged["auc_variance_delong"] = (
-        df_merged["auc_variance_sum"]
-        / (df_merged["n_strata"].clip(lower=1).astype(np.float64) ** 2)
+    df_report = build_delphi2m_auc_report(
+        df_unpooled,
+        period_col="horizon",
     )
-    df_merged = df_merged.drop(columns=["auc_variance_sum"])
-
-    keep_meta = [
-        c for c in [
-            "model_ckpt_path",
-            "config_path",
-            "target_mode",
-            "model_target_mode",
-            "dist_mode",
-            "time_mode",
-            "attn_mask_mode",
-            "readout_name",
-            "landmark_query_mode",
-            "landmark_token_mode",
-            "score_mode",
-            "eval_split",
-        ]
-        if c in df_unpooled.columns
-    ]
-    for col in keep_meta:
-        df_merged[col] = meta_info[col]
 
     output_path.mkdir(parents=True, exist_ok=True)
-    df_unpooled.to_csv(
-        output_path / "df_auc_landmark_unpooled.csv", index=False)
-    df_merged.to_csv(output_path / "df_auc_landmark.csv", index=False)
+    report_path = output_path / "df_auc_landmark_delphi2m_report.csv"
+    df_report.to_csv(report_path, index=False)
+    print(f"Saved Delphi2M-style landmark AUC report: {report_path}")
 
-    return df_unpooled, df_merged
+    return df_unpooled, df_report
 
 
 def main() -> None:
@@ -1308,7 +1250,12 @@ def main() -> None:
     parser.add_argument("--landmark_start", type=float, default=None)
     parser.add_argument("--landmark_stop", type=float, default=None)
     parser.add_argument("--landmark_step", type=float, default=None)
-    parser.add_argument("--horizons", type=str, default=None)
+    parser.add_argument(
+        "--horizons",
+        type=str,
+        default=None,
+        help="Comma-separated horizons in years; defaults to 0.1,1,5,10, where 0.1 is Delphi2M no gap.",
+    )
 
     parser.add_argument("--min_cases", type=int, default=None)
     parser.add_argument("--min_history_events", type=int, default=None)
@@ -1428,8 +1375,9 @@ def main() -> None:
             "Landmark ages are empty. Check landmark_start/landmark_stop/landmark_step.")
 
     horizons = np.asarray(
-        parse_float_list(cfg_get(args, cfg, "horizons", "1,5,10")) or [
-            1.0, 5.0, 10.0],
+        parse_float_list(
+            cfg_get(args, cfg, "horizons", "0.1,1,5,10")
+        ) or list(DEFAULT_DELPHI2M_PERIODS_YEARS),
         dtype=np.float32,
     )
     if horizons.size == 0:
@@ -1452,12 +1400,17 @@ def main() -> None:
 
     cfg_model = dict(cfg)
     cfg_model["dist_mode"] = dist_mode
+    model_architecture = resolve_model_architecture(cfg_model, state_dict)
+    cfg_model["model_architecture"] = model_architecture
+    print(f"Resolved model architecture: {model_architecture}")
 
     device = resolve_eval_device(args.device)
     if device.type == "cuda":
         torch.backends.cudnn.benchmark = True
 
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
 
     if (
         model_target_mode == "next_token"
@@ -1520,8 +1473,6 @@ def main() -> None:
         if model_target_mode == "next_token"
         else "direct_t_query"
     )
-    score_mode_out = f"{landmark_query_mode}_{score_mode}"
-
     num_workers_auc = int(
         cfg_get(args, cfg, "num_workers_auc", max(1, (os.cpu_count() or 2) - 1)))
     auc_task_chunk_size = int(cfg_get(args, cfg, "auc_task_chunk_size", 0))
@@ -1553,27 +1504,11 @@ def main() -> None:
     print(f"AUC workers: {num_workers_auc}")
     print(f"Output path: {output_path}")
 
-    meta_info = {
-        "score_mode": score_mode_out,
-        "eval_split": eval_split,
-        "model_ckpt_path": str(model_ckpt_path),
-        "config_path": str(config_path),
-        "target_mode": str(target_mode),
-        "model_target_mode": str(model_target_mode),
-        "dist_mode": str(dist_mode),
-        "time_mode": str(time_mode),
-        "attn_mask_mode": str(attn_mask_mode),
-        "readout_name": str(readout_name),
-        "landmark_query_mode": landmark_query_mode,
-        "landmark_token_mode": "no_event" if model_target_mode == "next_token" else "none",
-    }
-
     evaluate_landmark_auc(
         model=model,
         loader=loader,
         landmark_dataset=landmark_dataset,
         output_path=output_path,
-        labels_meta=labels_meta,
         disease_ids=disease_ids,
         disease_chunk_size=disease_chunk_size,
         score_mode=score_mode,
@@ -1590,7 +1525,6 @@ def main() -> None:
         use_amp=use_amp,
         hidden_cache_dtype=hidden_cache_dtype,
         logit_batch_size=logit_batch_size,
-        meta_info=meta_info,
     )
 
 

evaluate_event_free_survival.py

@@ -649,7 +649,9 @@ def main() -> None:
     cfg_model = dict(cfg)
     cfg_model["dist_mode"] = dist_mode
     device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, state_dict)
     model.eval()
 

evaluate_extra_info_attribution.py

@@ -758,7 +758,9 @@ def main() -> None:
     cfg_model = dict(cfg)
     cfg_model["dist_mode"] = dist_mode
     device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, state_dict)
     model.eval()
 

evaluate_single_disease_mortality_attribution.py

@@ -553,7 +553,9 @@ def main() -> None:
     device = resolve_eval_device(args.device)
     selected_token_mask = np.zeros(int(dataset.vocab_size), dtype=bool)
     selected_token_mask[np.asarray(scanned_disease_tokens, dtype=np.int64)] = True
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, state_dict)
     model.eval()
 

export_tquery_logits_hidden.py

@@ -180,7 +180,9 @@ def main() -> None:
     cfg_model = dict(cfg)
     cfg_model["dist_mode"] = dist_mode
     device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, state_dict)
     model.eval()
 

export_weibull_death_parameter_stats.py

@@ -381,7 +381,9 @@ def main() -> None:
     cfg_model = dict(cfg)
     cfg_model["dist_mode"] = dist_mode
     device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
+    model = build_model_from_dataset(
+        args, cfg_model, dataset, state_dict=state_dict
+    ).to(device)
     load_model_state(model, state_dict)
     model.eval()
 

model_architectures.py

@@ -0,0 +1,131 @@
+"""Model-architecture identifiers and checkpoint validation helpers."""
+
+from __future__ import annotations
+
+import re
+from collections.abc import Mapping
+
+
+TRANSFORMER_FFN_ARCHITECTURE = "transformer_ffn_v1"
+TRAJ_MIXER_ARCHITECTURE = "traj_mixer_v5"
+DEFAULT_MODEL_ARCHITECTURE = TRANSFORMER_FFN_ARCHITECTURE
+SUPPORTED_MODEL_ARCHITECTURES = (
+    TRANSFORMER_FFN_ARCHITECTURE,
+    TRAJ_MIXER_ARCHITECTURE,
+)
+
+
+_FFN_STATE_KEY = re.compile(
+    r"(?:^|\.)blocks\.\d+\.mlp\.w[123]\.(?:weight|bias)$"
+)
+_TRAJ_MIXER_STATE_KEY = re.compile(
+    r"(?:^|\.)blocks\.\d+\.mlp\.(?:"
+    r"norm\.(?:weight|bias)|"
+    r"intra_gate_proj|"
+    r"intra_value_proj|"
+    r"intra_output_proj|"
+    r"intra_gate_logits|"
+    r"gate_proj|"
+    r"value_proj|"
+    r"output_proj"
+    r")$"
+)
+
+
+def _validate_model_architecture(model_architecture: object) -> str:
+    if not isinstance(model_architecture, str):
+        raise ValueError(
+            "model_architecture must be one of "
+            f"{SUPPORTED_MODEL_ARCHITECTURES}, got {model_architecture!r}"
+        )
+    if model_architecture not in SUPPORTED_MODEL_ARCHITECTURES:
+        raise ValueError(
+            f"Unsupported model_architecture={model_architecture!r}; "
+            f"expected one of {SUPPORTED_MODEL_ARCHITECTURES}."
+        )
+    return model_architecture
+
+
+def detect_model_architecture_from_state_dict(
+    state_dict: Mapping[str, object],
+) -> str:
+    """Infer the architecture from block parameter names.
+
+    Detection deliberately accepts any ``blocks.<index>`` prefix rather than
+    assuming that block zero is present.
+    """
+
+    if not isinstance(state_dict, Mapping):
+        raise TypeError(
+            "state_dict must be a mapping, got "
+            f"{type(state_dict).__name__}"
+        )
+
+    has_ffn = False
+    has_traj_mixer = False
+    for raw_key in state_dict:
+        key = str(raw_key)
+        has_ffn = has_ffn or _FFN_STATE_KEY.search(key) is not None
+        has_traj_mixer = (
+            has_traj_mixer
+            or _TRAJ_MIXER_STATE_KEY.search(key) is not None
+        )
+        if has_ffn and has_traj_mixer:
+            raise ValueError(
+                "Checkpoint contains both Transformer FFN and TrajMixer "
+                "block parameters; its model architecture is ambiguous."
+            )
+
+    if has_ffn:
+        return TRANSFORMER_FFN_ARCHITECTURE
+    if has_traj_mixer:
+        return TRAJ_MIXER_ARCHITECTURE
+    raise ValueError(
+        "Could not detect model architecture from checkpoint parameters. "
+        "Expected a blocks.<index>.mlp FFN or TrajMixer parameter."
+    )
+
+
+def resolve_model_architecture(
+    config_or_marker: Mapping[str, object] | str | None = None,
+    state_dict: Mapping[str, object] | None = None,
+) -> str:
+    """Resolve and cross-check a configured and checkpoint architecture.
+
+    Every saved run must provide an explicit architecture marker. Checkpoint
+    parameter names are used only to verify that the marker describes the
+    weights being loaded.
+    """
+
+    if isinstance(config_or_marker, Mapping):
+        configured = config_or_marker.get("model_architecture")
+    elif isinstance(config_or_marker, str) or config_or_marker is None:
+        configured = config_or_marker
+    else:
+        raise TypeError(
+            "config_or_marker must be a config mapping, string, or None, got "
+            f"{type(config_or_marker).__name__}"
+        )
+
+    resolved_config = (
+        _validate_model_architecture(configured)
+        if configured is not None
+        else None
+    )
+    detected = (
+        detect_model_architecture_from_state_dict(state_dict)
+        if state_dict is not None
+        else None
+    )
+
+    if resolved_config is None:
+        raise ValueError(
+            "model_architecture is required; expected one of "
+            f"{SUPPORTED_MODEL_ARCHITECTURES}."
+        )
+    if detected is not None and resolved_config != detected:
+        raise ValueError(
+            "Configured model architecture conflicts with checkpoint: "
+            f"config={resolved_config!r}, checkpoint={detected!r}."
+        )
+    return resolved_config

models.py

@@ -6,11 +6,12 @@ import torch.nn.functional as F
 
 from backbones import (
     AgeSinusoidalEncoding,
-    GPTBlock,
     GaussianRBFTimeBasis,
     TimeRoPE,
     TokenAutoDiscretization,
+    build_backbone_block,
 )
+from model_architectures import resolve_model_architecture
 from targets import PAD_IDX
 
 
@@ -147,8 +148,7 @@ class DeepHealth(nn.Module):
         vocab_size: int,
         n_embd: int,
         n_head: int,
-        n_hist_layer: int,
-        n_tab_layer: int,
+        n_layer: int,
         n_types: int,
         n_cont_types: int,
         n_categories: int,
@@ -160,6 +160,7 @@ class DeepHealth(nn.Module):
         dist_mode: str = "exponential",  # "exponential", "weibull" or "mixed"
         extra_pool_reduce: str = "mean",
         dropout: float = 0.0,
+        model_architecture: str | None = None,
     ):
         super().__init__()
         if target_mode not in ["next_token", "all_future"]:
@@ -173,6 +174,9 @@ class DeepHealth(nn.Module):
                 "dist_mode must be either 'exponential', 'weibull' or 'mixed'")
         if extra_pool_reduce not in {"mean", "sum"}:
             raise ValueError("extra_pool_reduce must be either 'mean' or 'sum'")
+        if n_layer < 1:
+            raise ValueError(f"n_layer must be >= 1, got {n_layer}")
+        model_architecture = resolve_model_architecture(model_architecture)
         self.token_embedding = nn.Embedding(vocab_size, n_embd, padding_idx=0)
         self.gender_embedding = nn.Embedding(
             2, n_embd)  # Assuming binary gender
@@ -189,6 +193,8 @@ class DeepHealth(nn.Module):
         self.time_mode = time_mode
         self.dist_mode = dist_mode
         self.extra_pool_reduce = extra_pool_reduce
+        self.model_architecture = model_architecture
+        self.n_layer = n_layer
         self.n_embd = n_embd
         self.vocab_size = vocab_size
         nn.init.normal_(self.token_embedding.weight, mean=0.0, std=0.02)
@@ -208,26 +214,28 @@ class DeepHealth(nn.Module):
         if time_mode == "absolute":
             self.age_encoding = AgeSinusoidalEncoding(n_embd)
             self.blocks = nn.ModuleList([
-                GPTBlock(
+                build_backbone_block(
+                    model_architecture,
                     n_embd=n_embd,
                     n_head=n_head,
                     use_time_rope=False,
                     use_rbf_bias=False,
                     mlp_dropout=dropout,
-                ) for _ in range(n_hist_layer)
+                ) for _ in range(n_layer)
             ])
             self.rope = None
             self.rbf = None
         elif time_mode == "relative":
             self.age_encoding = None
             self.blocks = nn.ModuleList([
-                GPTBlock(
+                build_backbone_block(
+                    model_architecture,
                     n_embd=n_embd,
                     n_head=n_head,
                     use_time_rope=True,
                     use_rbf_bias=True,
                     mlp_dropout=dropout,
-                ) for _ in range(n_hist_layer)
+                ) for _ in range(n_layer)
             ])
             self.rope = TimeRoPE(n_embd // n_head)
             self.rbf = GaussianRBFTimeBasis(n_bases=16, max_time_diff=40.0)

run_missing_evaluations.sh

@@ -1,10 +1,11 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-# Run all non-wrapper evaluation scripts for every completed experiment under
-# runs/.  The script is written for Linux servers with bash 4.2.
+# Run all non-wrapper evaluation scripts for every completed current-format
+# experiment under runs/. The script is written for Linux servers with bash 4.2.
 
 cd "$(dirname "${BASH_SOURCE[0]}")"
+shopt -s globstar nullglob
 
 PYTHON_BIN="${PYTHON_BIN:-python}"
 DEVICE="${DEVICE:-cuda}"
@@ -81,6 +82,20 @@ run_dir_result_if_missing() {
   run_command "$@"
 }
 
+run_file_result_if_missing() {
+  local label="$1"
+  local result_dir="$2"
+  local required="$3"
+  shift 3
+
+  if [[ -s "${result_dir}/${required}" ]]; then
+    echo "    skip ${label}: found ${result_dir}/${required}"
+    return 0
+  fi
+
+  run_command "$@"
+}
+
 run_has_extra_info() {
   "${PYTHON_BIN}" - "$1" <<'PY'
 import json
@@ -115,8 +130,30 @@ raise SystemExit(0 if mode == "all_future" else 1)
 PY
 }
 
-for run_path in runs/*; do
-  [[ -d "${run_path}" ]] || continue
+run_has_current_model_config() {
+  "${PYTHON_BIN}" - "$1" <<'PY'
+import json
+import sys
+from pathlib import Path
+
+cfg_path = Path(sys.argv[1]) / "train_config.json"
+try:
+    cfg = json.loads(cfg_path.read_text(encoding="utf-8"))
+    n_layer = int(cfg.get("n_layer", 0))
+except Exception:
+    raise SystemExit(1)
+
+supported = {"transformer_ffn_v1", "traj_mixer_v5"}
+raise SystemExit(
+    0
+    if cfg.get("model_architecture") in supported and n_layer >= 1
+    else 1
+)
+PY
+}
+
+for config_path in runs/**/train_config.json; do
+  run_path="${config_path%/train_config.json}"
 
   echo "==> ${run_path}"
   if [[ ! -f "${run_path}/train_config.json" ]]; then
@@ -127,6 +164,10 @@ for run_path in runs/*; do
     echo "    skip run: missing best_model.pt"
     continue
   fi
+  if ! run_has_current_model_config "${run_path}"; then
+    echo "    skip run: config lacks current model_architecture/n_layer fields"
+    continue
+  fi
 
   common=()
   while IFS= read -r arg; do common+=("${arg}"); done < <(common_args_with_device "${run_path}")
@@ -137,18 +178,16 @@ for run_path in runs/*; do
   cpu_reduce_extra=()
   while IFS= read -r arg; do cpu_reduce_extra+=("${arg}"); done < <(cpu_reduce_args)
 
-  run_dir_result_if_missing \
+  run_file_result_if_missing \
     "evaluate_auc.py" \
     "${run_path}" \
-    "df_both.csv" \
-    "df_auc_unpooled.csv" \
+    "df_auc_delphi2m_report.csv" \
     "${PYTHON_BIN}" evaluate_auc.py "${common[@]}" "${auc_extra[@]}"
 
-  run_dir_result_if_missing \
+  run_file_result_if_missing \
     "evaluate_auc_v2.py" \
     "${run_path}" \
-    "df_auc_landmark.csv" \
-    "df_auc_landmark_unpooled.csv" \
+    "df_auc_landmark_delphi2m_report.csv" \
     "${PYTHON_BIN}" evaluate_auc_v2.py "${common[@]}" "${auc_extra[@]}"
 
   if ! run_is_all_future "${run_path}"; then

run_missing_training_runs.sh

@@ -10,7 +10,8 @@ set -euo pipefail
 #   all_future + relative time + mixed death/risk head
 #
 # This script only launches those missing training jobs. It intentionally does
-# not call evaluate_*.py and does not add extra random seeds.
+# not call evaluate_*.py and does not add extra random seeds. Set
+# MODEL_ARCHITECTURE=traj_mixer_v5 to run the TrajMixer variant.
 
 cd "$(dirname "${BASH_SOURCE[0]}")"
 
@@ -18,6 +19,8 @@ PYTHON_BIN="${PYTHON_BIN:-python}"
 DEVICE="${DEVICE:-cuda}"
 NUM_WORKERS="${NUM_WORKERS:-4}"
 PROGRESS_INTERVAL="${PROGRESS_INTERVAL:-20}"
+MODEL_ARCHITECTURE="${MODEL_ARCHITECTURE:-transformer_ffn_v1}"
+N_LAYER="${N_LAYER:-12}"
 
 TIME_MODE="relative"
 DIST_MODE="mixed"
@@ -36,8 +39,8 @@ COMMON_ARGS=(
   --min_future_events 1
   --n_embd 120
   --n_head 10
-  --n_hist_layer 12
-  --n_tab_layer 4
+  --n_layer "${N_LAYER}"
+  --model_architecture "${MODEL_ARCHITECTURE}"
   --n_bins 16
   --extra_pool_reduce mean
   --dropout 0.0
@@ -57,15 +60,23 @@ COMMON_ARGS=(
 
 already_trained() {
   local extra_file="$1"
-  "${PYTHON_BIN}" - "$TIME_MODE" "$DIST_MODE" "$extra_file" "$SEED" "$VALIDATION_QUERY_SEED" <<'PY'
+  "${PYTHON_BIN}" - "$TIME_MODE" "$DIST_MODE" "$extra_file" "$SEED" "$VALIDATION_QUERY_SEED" "$MODEL_ARCHITECTURE" "$N_LAYER" <<'PY'
 import json
 import sys
 from pathlib import Path
 
-time_mode, dist_mode, extra_file, seed, validation_query_seed = sys.argv[1:6]
+(
+    time_mode,
+    dist_mode,
+    extra_file,
+    seed,
+    validation_query_seed,
+    model_architecture,
+    n_layer,
+) = sys.argv[1:8]
 extra_name = Path(extra_file).name
 
-for config_path in Path("runs").glob("*/train_config.json"):
+for config_path in Path("runs").rglob("train_config.json"):
     try:
         cfg = json.loads(config_path.read_text(encoding="utf-8"))
     except Exception:
@@ -78,6 +89,8 @@ for config_path in Path("runs").glob("*/train_config.json"):
 
     if (
         cfg.get("model_target_mode") == "all_future"
+        and cfg.get("model_architecture") == model_architecture
+        and int(cfg.get("n_layer", -1)) == int(n_layer)
         and cfg.get("time_mode") == time_mode
         and cfg.get("dist_mode") == dist_mode
         and Path(str(cfg.get("extra_info_types_file", ""))).name == extra_name
@@ -100,7 +113,7 @@ train_if_missing() {
     return 2
   fi
 
-  echo "==> Checking ${label}: ${TIME_MODE} ${DIST_MODE} all_future with ${extra_file}"
+  echo "==> Checking ${label}: ${MODEL_ARCHITECTURE} n_layer=${N_LAYER} ${TIME_MODE} ${DIST_MODE} all_future with ${extra_file}"
   if existing_run="$(already_trained "$extra_file")"; then
     echo "    skip: already trained at ${existing_run}"
     return 0

test_model_architectures.py

@@ -0,0 +1,247 @@
+import unittest
+
+import torch
+
+from backbones import (
+    SwiGLU,
+    TrajMixer,
+    TrajMixerBlock,
+    TransformerFFNBlock,
+    build_backbone_block,
+)
+from model_architectures import (
+    TRAJ_MIXER_ARCHITECTURE,
+    TRANSFORMER_FFN_ARCHITECTURE,
+    detect_model_architecture_from_state_dict,
+    resolve_model_architecture,
+)
+from models import DeepHealth
+
+
+def _build_block(model_architecture: str):
+    return build_backbone_block(
+        model_architecture,
+        n_embd=12,
+        n_head=3,
+        use_time_rope=False,
+        use_rbf_bias=False,
+        mlp_dropout=0.0,
+    )
+
+
+def _as_model_state_dict(block: torch.nn.Module) -> dict[str, torch.Tensor]:
+    return {
+        f"blocks.0.{name}": value.detach().clone()
+        for name, value in block.state_dict().items()
+    }
+
+
+def _build_model(
+    model_architecture: str | None,
+    *,
+    n_layer: int = 1,
+) -> DeepHealth:
+    return DeepHealth(
+        vocab_size=8,
+        n_embd=12,
+        n_head=3,
+        n_layer=n_layer,
+        n_types=2,
+        n_cont_types=0,
+        n_categories=2,
+        cont_type_ids=[],
+        time_mode="absolute",
+        model_architecture=model_architecture,
+    )
+
+
+class ModelArchitectureFactoryTest(unittest.TestCase):
+    def test_factory_builds_both_architectures_with_expected_topology(self) -> None:
+        ffn_block = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
+        self.assertIsInstance(ffn_block, TransformerFFNBlock)
+        self.assertIsInstance(ffn_block.mlp, SwiGLU)
+        self.assertTrue(hasattr(ffn_block, "ln1"))
+        self.assertTrue(hasattr(ffn_block, "ln2"))
+
+        traj_block = _build_block(TRAJ_MIXER_ARCHITECTURE)
+        self.assertIsInstance(traj_block, TrajMixerBlock)
+        self.assertIsInstance(traj_block.mlp, TrajMixer)
+        self.assertTrue(hasattr(traj_block, "ln1"))
+        self.assertFalse(hasattr(traj_block, "ln2"))
+
+    def test_both_architectures_forward_and_backward(self) -> None:
+        for architecture in (
+            TRANSFORMER_FFN_ARCHITECTURE,
+            TRAJ_MIXER_ARCHITECTURE,
+        ):
+            with self.subTest(architecture=architecture):
+                torch.manual_seed(0)
+                block = _build_block(architecture)
+                x = torch.randn(2, 5, 12, requires_grad=True)
+
+                output = block(x)
+                self.assertEqual(output.shape, x.shape)
+                output.square().mean().backward()
+
+                self.assertIsNotNone(x.grad)
+                self.assertTrue(torch.isfinite(x.grad).all())
+                self.assertGreater(x.grad.abs().sum().item(), 0.0)
+                self.assertIsNotNone(block.attn.qkv.weight.grad)
+                self.assertGreater(
+                    block.attn.qkv.weight.grad.abs().sum().item(),
+                    0.0,
+                )
+
+                if architecture == TRANSFORMER_FFN_ARCHITECTURE:
+                    branch_parameters = (
+                        block.mlp.w1.weight,
+                        block.mlp.w2.weight,
+                        block.mlp.w3.weight,
+                    )
+                else:
+                    branch_parameters = (
+                        block.mlp.intra_gate_proj,
+                        block.mlp.intra_value_proj,
+                        block.mlp.output_proj,
+                    )
+                for parameter in branch_parameters:
+                    self.assertIsNotNone(parameter.grad)
+                    self.assertTrue(torch.isfinite(parameter.grad).all())
+                    self.assertGreater(parameter.grad.abs().sum().item(), 0.0)
+
+    def test_unknown_architecture_is_rejected(self) -> None:
+        with self.assertRaises(ValueError):
+            _build_block("unknown_architecture")
+        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
+            _build_model(None)
+
+    def test_deephealth_rejects_fewer_than_one_layer(self) -> None:
+        for n_layer in (0, -1):
+            with self.subTest(n_layer=n_layer):
+                with self.assertRaisesRegex(ValueError, "n_layer must be >= 1"):
+                    _build_model(
+                        TRANSFORMER_FFN_ARCHITECTURE,
+                        n_layer=n_layer,
+                    )
+
+    def test_deephealth_uses_factory_and_strictly_reloads_both_models(self) -> None:
+        for architecture, block_class in (
+            (TRANSFORMER_FFN_ARCHITECTURE, TransformerFFNBlock),
+            (TRAJ_MIXER_ARCHITECTURE, TrajMixerBlock),
+        ):
+            with self.subTest(architecture=architecture):
+                model = _build_model(architecture)
+                self.assertEqual(model.model_architecture, architecture)
+                self.assertIsInstance(model.blocks[0], block_class)
+                self.assertEqual(
+                    detect_model_architecture_from_state_dict(
+                        model.state_dict()
+                    ),
+                    architecture,
+                )
+
+                reloaded = _build_model(architecture)
+                incompatible = reloaded.load_state_dict(
+                    model.state_dict(),
+                    strict=True,
+                )
+                self.assertEqual(incompatible.missing_keys, [])
+                self.assertEqual(incompatible.unexpected_keys, [])
+
+
+class ModelArchitectureResolutionTest(unittest.TestCase):
+    def setUp(self) -> None:
+        self.ffn_block = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
+        self.traj_block = _build_block(TRAJ_MIXER_ARCHITECTURE)
+        self.ffn_state = _as_model_state_dict(self.ffn_block)
+        self.traj_state = _as_model_state_dict(self.traj_block)
+
+    def test_state_dict_detection_recognizes_both_architectures(self) -> None:
+        self.assertEqual(
+            detect_model_architecture_from_state_dict(self.ffn_state),
+            TRANSFORMER_FFN_ARCHITECTURE,
+        )
+        self.assertEqual(
+            detect_model_architecture_from_state_dict(self.traj_state),
+            TRAJ_MIXER_ARCHITECTURE,
+        )
+
+    def test_explicit_markers_resolve_when_checkpoint_matches(self) -> None:
+        for architecture, state_dict in (
+            (TRANSFORMER_FFN_ARCHITECTURE, self.ffn_state),
+            (TRAJ_MIXER_ARCHITECTURE, self.traj_state),
+        ):
+            with self.subTest(architecture=architecture):
+                self.assertEqual(
+                    resolve_model_architecture(
+                        {"model_architecture": architecture},
+                        state_dict,
+                    ),
+                    architecture,
+                )
+
+    def test_architecture_marker_is_required_for_checkpoint_loading(self) -> None:
+        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
+            resolve_model_architecture({}, self.ffn_state)
+        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
+            resolve_model_architecture(None, self.traj_state)
+
+    def test_explicit_marker_conflicting_with_state_dict_is_rejected(self) -> None:
+        conflicts = (
+            (TRANSFORMER_FFN_ARCHITECTURE, self.traj_state),
+            (TRAJ_MIXER_ARCHITECTURE, self.ffn_state),
+        )
+        for architecture, state_dict in conflicts:
+            with self.subTest(architecture=architecture):
+                with self.assertRaises(ValueError):
+                    resolve_model_architecture(
+                        {"model_architecture": architecture},
+                        state_dict,
+                    )
+
+    def test_unknown_marker_and_ambiguous_state_dict_are_rejected(self) -> None:
+        with self.assertRaises(ValueError):
+            resolve_model_architecture(
+                {"model_architecture": "traj_mixer_v4"}
+            )
+
+        ambiguous_state = dict(self.ffn_state)
+        ambiguous_state.update(self.traj_state)
+        with self.assertRaises(ValueError):
+            detect_model_architecture_from_state_dict(ambiguous_state)
+
+        with self.assertRaises(ValueError):
+            detect_model_architecture_from_state_dict(
+                {"token_embedding.weight": torch.empty(2, 2)}
+            )
+
+    def test_ffn_block_schema_is_stable_and_strictly_loadable(self) -> None:
+        expected_keys = {
+            "attn.time_bias_scale",
+            "attn.qkv.weight",
+            "attn.out_proj.weight",
+            "attn.rbf_proj.weight",
+            "mlp.w1.weight",
+            "mlp.w1.bias",
+            "mlp.w2.weight",
+            "mlp.w2.bias",
+            "mlp.w3.weight",
+            "mlp.w3.bias",
+            "ln1.weight",
+            "ln1.bias",
+            "ln2.weight",
+            "ln2.bias",
+        }
+        state = self.ffn_block.state_dict()
+        self.assertSetEqual(set(state), expected_keys)
+        self.assertEqual(tuple(state["mlp.w1.weight"].shape), (30, 12))
+        self.assertEqual(tuple(state["mlp.w3.weight"].shape), (12, 30))
+
+        reloaded = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
+        incompatible = reloaded.load_state_dict(state, strict=True)
+        self.assertEqual(incompatible.missing_keys, [])
+        self.assertEqual(incompatible.unexpected_keys, [])
+
+
+if __name__ == "__main__":
+    unittest.main()

test_temporal_attention.py

@@ -0,0 +1,46 @@
+import unittest
+
+import torch
+
+from backbones import TemporalAttention
+
+
+class TemporalAttentionTest(unittest.TestCase):
+    def test_zero_rbf_bias_has_live_projection_gradient(self) -> None:
+        torch.manual_seed(0)
+        attention = TemporalAttention(
+            n_embd=12,
+            n_head=3,
+            use_time_rope=False,
+            use_rbf_bias=True,
+        )
+        features = torch.randn(2, 4, 4, 16)
+        target = torch.randn(2, 4, 4, 3)
+
+        initial_bias = (
+            attention.time_bias_scale.tanh()
+            * attention.rbf_proj(features)
+        )
+        torch.testing.assert_close(initial_bias, torch.zeros_like(initial_bias))
+
+        (initial_bias * target).sum().backward()
+        projection_grad = attention.rbf_proj.weight.grad
+        self.assertIsNotNone(projection_grad)
+        self.assertGreater(projection_grad.abs().sum().item(), 0.0)
+
+        with torch.no_grad():
+            attention.rbf_proj.weight.add_(projection_grad, alpha=-1e-3)
+        attention.zero_grad(set_to_none=True)
+        updated_bias = (
+            attention.time_bias_scale.tanh()
+            * attention.rbf_proj(features)
+        )
+        (updated_bias * target).sum().backward()
+
+        scale_grad = attention.time_bias_scale.grad
+        self.assertIsNotNone(scale_grad)
+        self.assertGreater(scale_grad.abs().item(), 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()

test_traj_mixer.py

@@ -0,0 +1,166 @@
+import unittest
+
+import torch
+
+from backbones import TrajMixer
+
+
+class TrajMixerTest(unittest.TestCase):
+    def test_default_shape_parameters_and_initialization(self) -> None:
+        mixer = TrajMixer(
+            n_embd=120,
+            n_head=10,
+            dropout=0.0,
+        )
+
+        x = torch.randn(2, 7, 120)
+        self.assertEqual(mixer(x).shape, x.shape)
+        self.assertEqual(sum(p.numel() for p in mixer.parameters()), 32_040)
+        self.assertEqual(tuple(mixer.norm.normalized_shape), (120,))
+        self.assertEqual(tuple(mixer.intra_gate_logits.shape), (10, 12))
+        torch.testing.assert_close(
+            torch.sigmoid(mixer.intra_gate_logits.detach()),
+            torch.full((10, 12), 0.1),
+        )
+        self.assertEqual(mixer.intra_hidden, 48)
+        self.assertEqual(
+            tuple(mixer.intra_gate_proj.shape),
+            (10, 12, 48),
+        )
+        self.assertEqual(
+            tuple(mixer.intra_value_proj.shape),
+            (10, 12, 48),
+        )
+        self.assertEqual(
+            tuple(mixer.intra_output_proj.shape),
+            (10, 48, 12),
+        )
+        self.assertEqual(mixer.hidden_group, 40)
+        self.assertEqual(tuple(mixer.gate_proj.shape), (12, 10, 40))
+        self.assertEqual(tuple(mixer.value_proj.shape), (12, 10, 40))
+        self.assertEqual(tuple(mixer.output_proj.shape), (12, 40, 10))
+
+    def test_zero_final_output_projection_makes_mixer_identity(self) -> None:
+        torch.manual_seed(0)
+        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        with torch.no_grad():
+            mixer.output_proj.zero_()
+        x = torch.randn(2, 5, 12)
+        torch.testing.assert_close(mixer(x), x)
+
+    def test_forward_matches_single_outer_residual_formula(self) -> None:
+        torch.manual_seed(0)
+        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        mixer.eval()
+        x = torch.randn(2, 5, 12)
+
+        grouped = mixer.norm(x).reshape(2, 5, 3, 4)
+        intra_output = mixer._intra_mix(grouped)
+        static_gate = torch.sigmoid(mixer.intra_gate_logits).view(
+            1, 1, 3, 4
+        )
+        mixed_input = grouped + static_gate * intra_output
+        update = mixer._cross_mix(mixed_input).reshape(2, 5, 12)
+
+        torch.testing.assert_close(mixer(x), x + update)
+
+    def test_intra_stage_is_independent_across_groups(self) -> None:
+        torch.manual_seed(0)
+        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        mixer.eval()
+
+        grouped = torch.randn(2, 4, 3, 4)
+        changed = grouped.clone()
+        changed[:, :, 1, :] += torch.randn_like(changed[:, :, 1, :])
+
+        original_out = mixer._intra_mix(grouped)
+        changed_out = mixer._intra_mix(changed)
+        unchanged_groups = torch.tensor([0, 2])
+        torch.testing.assert_close(
+            original_out.index_select(2, unchanged_groups),
+            changed_out.index_select(2, unchanged_groups),
+        )
+
+    def test_cross_stage_mixes_groups_without_mixing_coordinates(self) -> None:
+        mixer = TrajMixer(6, n_head=3, dropout=0.0)
+        mixer.eval()
+        with torch.no_grad():
+            mixer.gate_proj.zero_()
+            mixer.value_proj.zero_()
+            mixer.output_proj.zero_()
+
+            # Coordinate 0 reads group 0 through hidden unit 0 and writes it
+            # into group 1. Coordinate 1 must remain independent.
+            mixer.gate_proj[0, 0, 0] = 1.0
+            mixer.value_proj[0, 0, 0] = 1.0
+            mixer.output_proj[0, 0, 1] = 1.0
+
+        grouped = torch.tensor(
+            [[[
+                [-1.0, 4.0],
+                [0.0, 5.0],
+                [1.0, 6.0],
+            ]]]
+        )
+        changed = grouped.clone()
+        changed[0, 0, 0, 0] = 2.0
+
+        original_out = mixer._cross_mix(grouped)
+        changed_out = mixer._cross_mix(changed)
+
+        self.assertNotEqual(
+            original_out[0, 0, 1, 0].item(),
+            changed_out[0, 0, 1, 0].item(),
+        )
+        torch.testing.assert_close(
+            original_out[..., 1],
+            changed_out[..., 1],
+        )
+
+    def test_mixer_does_not_mix_sequence_positions(self) -> None:
+        torch.manual_seed(0)
+        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        mixer.eval()
+        x = torch.randn(2, 5, 12)
+        changed = x.clone()
+        changed[:, 3, :] += torch.randn_like(changed[:, 3, :])
+
+        original_out = mixer(x)
+        changed_out = mixer(changed)
+        unchanged_positions = torch.tensor([0, 1, 2, 4])
+        torch.testing.assert_close(
+            original_out.index_select(1, unchanged_positions),
+            changed_out.index_select(1, unchanged_positions),
+        )
+
+    def test_gradients_reach_every_projection_family(self) -> None:
+        torch.manual_seed(1)
+        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        x = torch.randn(2, 4, 12, requires_grad=True)
+
+        mixer(x).square().mean().backward()
+
+        self.assertIsNotNone(x.grad)
+        self.assertTrue(torch.isfinite(x.grad).all())
+        self.assertGreater(x.grad.abs().sum().item(), 0.0)
+        projection_names = (
+            "intra_gate_proj",
+            "intra_value_proj",
+            "intra_output_proj",
+            "gate_proj",
+            "value_proj",
+            "output_proj",
+        )
+        for name in projection_names:
+            parameter = getattr(mixer, name)
+            self.assertIsNotNone(parameter.grad, name)
+            self.assertTrue(torch.isfinite(parameter.grad).all(), name)
+            self.assertGreater(parameter.grad.abs().sum().item(), 0.0, name)
+
+    def test_invalid_group_partition_is_rejected(self) -> None:
+        with self.assertRaisesRegex(ValueError, "divisible"):
+            TrajMixer(n_embd=121, n_head=10)
+
+
+if __name__ == "__main__":
+    unittest.main()

train_all_future.py

@@ -27,12 +27,17 @@ from tqdm.auto import tqdm
 
 from dataset import AllFutureHealthDataset, all_future_collate_fn
 from losses import build_loss
+from model_architectures import (
+    DEFAULT_MODEL_ARCHITECTURE,
+    SUPPORTED_MODEL_ARCHITECTURES,
+)
 from models import DeepHealth
 from targets import CHECKUP_IDX, PAD_IDX
 from train_util import (
     configure_torch_for_training,
     create_unique_run_dir,
     format_extra_info_types,
+    get_model_parameter_counts,
     load_extra_info_types_file,
     resolve_device,
     save_checkpoint,
@@ -64,6 +69,7 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument("--data_prefix", type=str, default="ukb")
     parser.add_argument("--labels_file", type=str, default="labels.csv")
+    parser.add_argument("--runs_root", type=str, default="runs")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--extra_info_types_file", type=str, default=None)
 
@@ -79,8 +85,7 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument("--n_embd", type=int, default=120)
     parser.add_argument("--n_head", type=int, default=10)
-    parser.add_argument("--n_hist_layer", type=int, default=12)
-    parser.add_argument("--n_tab_layer", type=int, default=4)
+    parser.add_argument("--n_layer", type=int, default=12)
     parser.add_argument("--n_bins", type=int, default=16)
     parser.add_argument("--extra_pool_reduce", type=str, default="mean",
                         choices=["mean", "sum"])
@@ -89,6 +94,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--dist_mode", type=str, default="exponential",
                         choices=["exponential", "weibull", "mixed"])
     parser.add_argument("--dropout", type=float, default=0.0)
+    parser.add_argument(
+        "--model_architecture",
+        type=str,
+        default=DEFAULT_MODEL_ARCHITECTURE,
+        choices=SUPPORTED_MODEL_ARCHITECTURES,
+    )
 
     parser.add_argument("--batch_size", type=int, default=128)
     parser.add_argument("--base_lr", type=float, default=3e-4)
@@ -147,8 +158,7 @@ def build_model(args: argparse.Namespace, dataset: AllFutureHealthDataset) -> De
         vocab_size=dataset.vocab_size,
         n_embd=args.n_embd,
         n_head=args.n_head,
-        n_hist_layer=args.n_hist_layer,
-        n_tab_layer=args.n_tab_layer,
+        n_layer=args.n_layer,
         n_types=dataset.n_types,
         n_cont_types=dataset.n_cont_types,
         n_categories=dataset.n_categories,
@@ -159,6 +169,7 @@ def build_model(args: argparse.Namespace, dataset: AllFutureHealthDataset) -> De
         time_mode=args.time_mode,
         dist_mode=args.dist_mode,
         dropout=args.dropout,
+        model_architecture=args.model_architecture,
     )
 
 
@@ -298,6 +309,7 @@ def build_metadata(
         "dataset_class": "AllFutureHealthDataset",
         "collate_fn": "all_future_collate_fn",
         "model_class": "DeepHealth",
+        "model_architecture": args.model_architecture,
         "model_target_mode": "all_future",
         "target_mode": "all_future",
         "dist_mode": args.dist_mode,
@@ -335,12 +347,14 @@ def main() -> None:
     configure_torch_for_training(device)
 
     run_dir, run_name = create_unique_run_dir(
-        lambda timestamp: f"{args.time_mode}_{args.dist_mode}_all_future_pure_disease_{timestamp}"
+        lambda timestamp: f"{args.time_mode}_{args.dist_mode}_all_future_pure_disease_{timestamp}",
+        runs_root=Path(args.runs_root) / args.model_architecture,
     )
     logger = setup_logging(run_dir)
 
     logger.info(f"Starting all-future training run: {run_name}")
     logger.info(f"Device: {device}")
+    logger.info(f"Model architecture: {args.model_architecture}")
     logger.info(f"extra_info_types: {format_extra_info_types(args.extra_info_types)}")
 
     logger.info("Loading all-future datasets...")
@@ -434,6 +448,12 @@ def main() -> None:
     )
 
     model = build_model(args, train_dataset).to(device)
+    parameter_counts = get_model_parameter_counts(model)
+    logger.info(
+        "Model parameters: "
+        f"total={parameter_counts['model_parameter_count']:,}, "
+        f"trainable={parameter_counts['trainable_parameter_count']:,}"
+    )
     optimizer = AdamW(
         model.parameters(),
         lr=args.base_lr,
@@ -443,10 +463,14 @@ def main() -> None:
     criterion = build_criterion(args, train_dataset)
     adaptive_lr = args.base_lr * math.sqrt(args.batch_size / 128)
 
+    train_metadata = build_metadata(
+        args, train_dataset, run_name, train_subset, val_subset, test_subset
+    )
+    train_metadata.update(parameter_counts)
     save_config(
         args,
         run_dir / "train_config.json",
-        extra=build_metadata(args, train_dataset, run_name, train_subset, val_subset, test_subset),
+        extra=train_metadata,
     )
 
     best_val = float("inf")

train_next_step.py

@@ -24,6 +24,10 @@ from tqdm.auto import tqdm
 
 from dataset import HealthDataset, collate_fn
 from losses import build_loss
+from model_architectures import (
+    DEFAULT_MODEL_ARCHITECTURE,
+    SUPPORTED_MODEL_ARCHITECTURES,
+)
 from models import DeepHealth, DeepHealthOutput
 from readouts import build_readout
 from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX
@@ -31,6 +35,7 @@ from train_util import (
     configure_torch_for_training,
     create_unique_run_dir,
     format_extra_info_types,
+    get_model_parameter_counts,
     load_extra_info_types_file,
     resolve_device,
     save_checkpoint,
@@ -61,6 +66,7 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument("--data_prefix", type=str, default="ukb")
     parser.add_argument("--labels_file", type=str, default="labels.csv")
+    parser.add_argument("--runs_root", type=str, default="runs")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--extra_info_types_file", type=str, default=None)
     parser.add_argument("--no_event_interval_years", type=float, default=5.0)
@@ -75,14 +81,19 @@ def parse_args() -> argparse.Namespace:
 
     parser.add_argument("--n_embd", type=int, default=120)
     parser.add_argument("--n_head", type=int, default=10)
-    parser.add_argument("--n_hist_layer", type=int, default=12)
-    parser.add_argument("--n_tab_layer", type=int, default=4)
+    parser.add_argument("--n_layer", type=int, default=12)
     parser.add_argument("--n_bins", type=int, default=16)
     parser.add_argument("--extra_pool_reduce", type=str, default="mean",
                         choices=["mean", "sum"])
     parser.add_argument("--time_mode", type=str, default="relative",
                         choices=["relative", "absolute"])
     parser.add_argument("--dropout", type=float, default=0.0)
+    parser.add_argument(
+        "--model_architecture",
+        type=str,
+        default=DEFAULT_MODEL_ARCHITECTURE,
+        choices=SUPPORTED_MODEL_ARCHITECTURES,
+    )
 
     parser.add_argument("--target_mode", type=str, default="uts",
                         choices=["delphi2m", "uts"])
@@ -152,8 +163,7 @@ def build_model(args: argparse.Namespace, dataset: HealthDataset) -> DeepHealth:
         vocab_size=dataset.vocab_size,
         n_embd=args.n_embd,
         n_head=args.n_head,
-        n_hist_layer=args.n_hist_layer,
-        n_tab_layer=args.n_tab_layer,
+        n_layer=args.n_layer,
         n_types=dataset.n_types,
         n_cont_types=dataset.n_cont_types,
         n_categories=dataset.n_categories,
@@ -164,6 +174,7 @@ def build_model(args: argparse.Namespace, dataset: HealthDataset) -> DeepHealth:
         time_mode=args.time_mode,
         dist_mode="exponential",
         dropout=args.dropout,
+        model_architecture=args.model_architecture,
     )
 
 
@@ -484,6 +495,7 @@ def build_metadata(
         "dataset_class": "NextStepHealthDataset",
         "collate_fn": "next_step_collate_fn",
         "model_class": "DeepHealth",
+        "model_architecture": args.model_architecture,
         "model_target_mode": "next_token",
         "target_mode": args.target_mode,
         "dist_mode": "exponential",
@@ -521,12 +533,14 @@ def main() -> None:
         lambda timestamp: (
             f"{args.time_mode}_exponential_next_token_{args.target_mode}_"
             f"gap_{args.no_event_interval_years:g}y_{timestamp}"
-        )
+        ),
+        runs_root=Path(args.runs_root) / args.model_architecture,
     )
     logger = setup_logging(run_dir)
 
     logger.info(f"Starting next-step training run: {run_name}")
     logger.info(f"Device: {device}")
+    logger.info(f"Model architecture: {args.model_architecture}")
     logger.info(f"extra_info_types: {format_extra_info_types(args.extra_info_types)}")
     logger.info(f"readout={args.readout_name}, target_mode={args.target_mode}")
 
@@ -596,6 +610,12 @@ def main() -> None:
     )
 
     model = build_model(args, dataset).to(device)
+    parameter_counts = get_model_parameter_counts(model)
+    logger.info(
+        "Model parameters: "
+        f"total={parameter_counts['model_parameter_count']:,}, "
+        f"trainable={parameter_counts['trainable_parameter_count']:,}"
+    )
     readout = build_next_step_readout(args).to(device)
     criterion = build_next_step_loss(args)
     optimizer = AdamW(
@@ -606,10 +626,14 @@ def main() -> None:
     )
     adaptive_lr = args.base_lr * math.sqrt(args.batch_size / 128)
 
+    train_metadata = build_metadata(
+        args, dataset, run_name, train_subset, val_subset, test_subset
+    )
+    train_metadata.update(parameter_counts)
     save_config(
         args,
         run_dir / "train_config.json",
-        extra=build_metadata(args, dataset, run_name, train_subset, val_subset, test_subset),
+        extra=train_metadata,
     )
 
     best_val = float("inf")

train_util.py

@@ -300,6 +300,20 @@ def build_optimizer(args: Any, model: DeepHealth) -> AdamW:
     )
 
 
+def get_model_parameter_counts(model: torch.nn.Module) -> Dict[str, int]:
+    """Return stable total and trainable parameter counts."""
+    return {
+        "model_parameter_count": sum(
+            parameter.numel() for parameter in model.parameters()
+        ),
+        "trainable_parameter_count": sum(
+            parameter.numel()
+            for parameter in model.parameters()
+            if parameter.requires_grad
+        ),
+    }
+
+
 def set_optimizer_lr(optimizer: AdamW, lr: float) -> None:
     for param_group in optimizer.param_groups:
         param_group["lr"] = lr