Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261012T143729Z-strictpairs-recovery-two-host/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T143729Z-strictpairs-recovery-two-host/snapshot/16/ft_sdd.py

@@ -0,0 +1,503 @@
+#!/usr/bin/env python3
+"""Fine-tune the final PBISC-SDD model inside one paired dataset fold.
+
+Training pairs are (condition of row r, one retained cell of row r).  The condition comes from a condition NPZ
+written by prepare_dataset.py (real bulk or pseudobulk, fixed-depth policy) and goes through the checkpoint's own
+conditioner exactly as the standalone sampler does (sample_sdd_standalone.load_conditions), then
+condition_with_measurement_context(base, bulk_measurement_fraction, output_availability_mask).  The loss is the
+pretraining loss (SparseDataDiffusion.training_loss: measured-only value L2 + support BCE, self-conditioning),
+with bf16 autocast, AdamW and gradient clipping as in pretraining.
+
+Rows: fold k's test rows are never loaded.  Training rows = the other folds minus the inner-validation rows;
+inner-validation rows give a fixed-draw diffusion loss every --eval-every steps, and the EMA state with the lowest
+inner-validation loss is kept (early stopping with --patience evaluations).
+
+Modes (what is trainable):
+  full     every denoiser parameter
+  condenc  only denoiser.condition_encoder (the bulk -> context map)
+  lora     low-rank updates W = W0 + (alpha/r) B A on every nn.Linear of the denoiser (B starts at zero);
+           merged into the weights before saving
+
+Batches are donor-balanced: a row is drawn uniformly, then one of its cells uniformly.
+
+The output checkpoint keeps the input checkpoint's schema and self-contained keys, with model_state and
+ema_state.model_state set to the selected weights, so scripts/sample_sdd_standalone.py samples it unchanged.
+src/pbisc_sdd is sealed and is only imported.
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+from torch import nn
+import torch.nn.utils.parametrize as parametrize
+
+M8 = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion")
+for p in (M8 / "src", M8 / "scripts"):
+    if str(p) not in sys.path:
+        sys.path.insert(0, str(p))
+import sample_sdd_standalone as SS  # noqa: E402
+from pbisc_sdd.data import condition_with_measurement_context  # noqa: E402
+
+SCHEMA = "pbisc_sdd.ft_intradataset.run.v1"
+KEEP_KEYS = ("schema_version", "model_config", "diffusion_config", "value_scaler", "conditioner_state",
+             "gene_positions", "gene_ids", "gene_symbols", "gene_measurement_coverage",
+             "full_gene_measurement_coverage", "corpus_fingerprint", "experiment_config", "proportion_hint",
+             "training_contract")
+
+
+def sha256_file(path: Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(1 << 24), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def log(msg: str) -> None:
+    print(f"{datetime.now().isoformat(timespec='seconds')} {msg}", flush=True)
+
+
+# ----------------------------------------------------------------------------------------------- LoRA
+class LoRADelta(nn.Module):
+    """Parametrization W -> W + scale * B @ A (B zero-initialised, so the start equals the pretrained W)."""
+
+    def __init__(self, out_features: int, in_features: int, rank: int, alpha: float) -> None:
+        super().__init__()
+        self.A = nn.Parameter(torch.empty(rank, in_features))
+        self.B = nn.Parameter(torch.zeros(out_features, rank))
+        nn.init.kaiming_uniform_(self.A, a=math.sqrt(5))
+        self.scale = float(alpha) / float(rank)
+
+    def forward(self, weight: torch.Tensor) -> torch.Tensor:
+        return weight + self.scale * (self.B @ self.A).to(dtype=weight.dtype)
+
+
+def add_lora(model: nn.Module, rank: int, alpha: float) -> list[str]:
+    names = []
+    for name, module in model.named_modules():
+        if isinstance(module, nn.Linear):
+            delta = LoRADelta(module.out_features, module.in_features, rank, alpha).to(
+                device=module.weight.device, dtype=module.weight.dtype)
+            parametrize.register_parametrization(module, "weight", delta)
+            names.append(name)
+    return names
+
+
+def merge_lora(model: nn.Module) -> None:
+    for module in model.modules():
+        if isinstance(module, nn.Linear) and parametrize.is_parametrized(module, "weight"):
+            parametrize.remove_parametrizations(module, "weight", leave_parametrized=True)
+
+
+def set_trainable(denoiser: nn.Module, mode: str, rank: int, alpha: float) -> dict:
+    for p in denoiser.parameters():
+        p.requires_grad_(False)
+    info: dict = {"mode": mode}
+    if mode == "full":
+        for p in denoiser.parameters():
+            p.requires_grad_(True)
+    elif mode == "condenc":
+        for p in denoiser.condition_encoder.parameters():
+            p.requires_grad_(True)
+    elif mode == "lora":
+        info["lora_layers"] = add_lora(denoiser, rank, alpha)
+        info["lora_rank"], info["lora_alpha"] = rank, alpha
+        for name, p in denoiser.named_parameters():
+            p.requires_grad_(name.endswith(".A") or name.endswith(".B"))
+    else:
+        raise SystemExit(f"unknown mode {mode}")
+    trainable = [n for n, p in denoiser.named_parameters() if p.requires_grad]
+    info["n_trainable"] = int(sum(p.numel() for p in denoiser.parameters() if p.requires_grad))
+    info["n_total_base"] = int(sum(p.numel() for n, p in denoiser.named_parameters()
+                                   if not (n.endswith(".A") or n.endswith(".B"))))
+    info["trainable_tensors"] = len(trainable)
+    return info
+
+
+# ----------------------------------------------------------------------------------------------- data
+class Cells:
+    """Retained cells on the 6k panel (CSR, rows sorted by dataset row)."""
+
+    def __init__(self, path: Path, panel_gene_ids: np.ndarray) -> None:
+        z = np.load(path, allow_pickle=False)
+        self.indptr = z["indptr"].astype(np.int64)
+        self.indices = z["indices"].astype(np.int64)
+        self.data = z["data"].astype(np.float32)
+        self.row_sample = z["row_sample"].astype(np.int64)
+        self.n_genes = int(z["shape"][1])
+        if not np.array_equal(z["panel_gene_ids"].astype(str), panel_gene_ids.astype(str)):
+            raise SystemExit("cells_panel6k columns differ from the checkpoint gene panel")
+        if bool((np.diff(self.row_sample) < 0).any()):
+            raise SystemExit("cells are not sorted by dataset row")
+        n_rows = int(self.row_sample.max()) + 1
+        self.start = np.searchsorted(self.row_sample, np.arange(n_rows), side="left")
+        self.count = np.searchsorted(self.row_sample, np.arange(n_rows), side="right") - self.start
+
+    def dense(self, cells: np.ndarray) -> np.ndarray:
+        s, e = self.indptr[cells], self.indptr[cells + 1]
+        lens = e - s
+        idx = np.concatenate([np.arange(a, b) for a, b in zip(s, e)]) if len(cells) else np.zeros(0, np.int64)
+        out = np.zeros((len(cells), self.n_genes), dtype=np.float32)
+        out[np.repeat(np.arange(len(cells)), lens), self.indices[idx]] = self.data[idx]
+        return out
+
+
+def draw_batch(cells: Cells, rows: np.ndarray, batch: int, rng: np.random.Generator) -> tuple[np.ndarray, np.ndarray]:
+    r = rows[rng.integers(0, len(rows), size=batch)]
+    c = cells.start[r] + np.floor(rng.random(batch) * cells.count[r]).astype(np.int64)
+    return r, c
+
+
+# ----------------------------------------------------------------------------------------------- main
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--checkpoint", type=Path, required=True)
+    ap.add_argument("--checkpoint-sha", required=True)
+    ap.add_argument("--dataset-dir", type=Path, required=True)
+    ap.add_argument("--condition-npz", type=Path, required=True, help="all-row condition NPZ used for training")
+    ap.add_argument("--fold", type=int, required=True)
+    ap.add_argument("--mode", choices=("full", "condenc", "lora"), required=True)
+    ap.add_argument("--lora-rank", type=int, default=16)
+    ap.add_argument("--lora-alpha", type=float, default=16.0)
+    ap.add_argument("--lr", type=float, required=True)
+    ap.add_argument("--warmup", type=int, default=200)
+    ap.add_argument("--batch-size", type=int, default=256)
+    ap.add_argument("--max-steps", type=int, default=20000)
+    ap.add_argument("--eval-every", type=int, default=250)
+    ap.add_argument("--patience", type=int, default=8)
+    ap.add_argument("--ema-decay", type=float, default=0.999)
+    ap.add_argument("--val-cells-per-row", type=int, default=256)
+    ap.add_argument("--val-repeats", type=int, default=2)
+    ap.add_argument("--seed", type=int, default=20260927)
+    ap.add_argument("--out-dir", type=Path, required=True)
+    ap.add_argument("--device", default="cuda")
+    ap.add_argument("--max-cuda-gib", type=float, default=8.0)
+    ap.add_argument("--min-system-gib", type=float, default=32.0)
+    ap.add_argument("--smoke", action="store_true", help="tiny run for tests (few steps, CPU allowed)")
+    ap.add_argument("--train-n", type=int, default=0, help="learning curve: number of training donors to keep")
+    ap.add_argument("--train-donors-file", default="", help="few-pair scenario: train on these donor_ids (one per "
+                    "line), no inner validation, every other usable row is test; --fold is ignored")
+    ap.add_argument("--fixed-steps", type=int, default=0, help="train exactly this many steps without inner "
+                    "validation and keep the final EMA state (for when no pairs can be spared for early stopping)")
+    ap.add_argument("--subset-seed", type=int, default=0)
+    ap.add_argument("--strata-regex", default="", help="groups matching this regex form one stratum, the rest another")
+    ap.add_argument("--support-manifest", type=Path, default=None,
+                    help="strict paired support.json: exact sample IDs, no inner validation; requires --fixed-steps 3000")
+    a = ap.parse_args(argv)
+
+    if a.support_manifest is not None:
+        if a.mode != "lora" or a.train_donors_file or a.train_n or a.fixed_steps != 3000 or a.smoke:
+            raise SystemExit("strict support requires LoRA, --fixed-steps 3000, no donor/subset override or smoke")
+        strict_tools = M8 / "plans/paper_figures_strictpairs_20261012/tools"
+        sys.path.insert(0, str(strict_tools))
+        from support_manifest import load_manifest, validate_existing_run
+        support_preflight = load_manifest(a.support_manifest, pd.read_csv(a.dataset_dir / "folds.csv",
+                                                                           dtype={"sample_id": str, "donor_id": str}))
+        if support_preflight["cohort"] != a.dataset_dir.name:
+            raise SystemExit("support cohort differs from dataset directory")
+    a.out_dir.mkdir(parents=True, exist_ok=True)
+    done = a.out_dir / "checkpoint_ft.pt"
+    if done.exists() and (a.out_dir / "RUN.json").exists():
+        if a.support_manifest is not None:
+            stored = json.loads((a.out_dir / "RUN.json").read_text())
+            validate_existing_run(support_preflight, stored, fixed_steps=a.fixed_steps,
+                                  lora_rank=a.lora_rank, source_checkpoint_sha256=a.checkpoint_sha,
+                                  condition_npz_sha256=sha256_file(a.condition_npz),
+                                  lr=a.lr, lora_alpha=a.lora_alpha, warmup=a.warmup,
+                                  batch_size=a.batch_size, ema_decay=a.ema_decay, seed=a.seed)
+            if sha256_file(a.checkpoint) != a.checkpoint_sha:
+                raise SystemExit("source checkpoint differs from requested hash")
+            if stored.get("checkpoint_sha256") != sha256_file(done):
+                raise SystemExit("existing strict checkpoint differs from RUN.json hash")
+        log(f"{done} exists; nothing to do")
+        return 0
+    if a.support_manifest is not None and (done.exists() or (a.out_dir / "RUN.json").exists()):
+        raise SystemExit("strict output is incomplete; inspect rather than overwrite")
+    avail = int(open("/proc/meminfo").read().split("MemAvailable:")[1].split()[0]) / 1048576
+    if avail < a.min_system_gib:
+        raise SystemExit(f"MemAvailable {avail:.1f} GiB < floor {a.min_system_gib}")
+    got = sha256_file(a.checkpoint)
+    if got != a.checkpoint_sha:
+        raise SystemExit(f"checkpoint sha {got[:12]} != {a.checkpoint_sha[:12]}")
+    cells_path = a.dataset_dir / "cells_panel6k.npz"
+    isolation = None
+    if a.support_manifest is not None:
+        from support_input_isolation import materialize
+        cells_path, isolation = materialize(a.support_manifest, dataset_dir=a.dataset_dir)
+    device = torch.device(a.device)
+    if device.type == "cuda" and device.index is None:
+        device = torch.device("cuda", 0)
+    if device.type == "cuda":
+        total = torch.cuda.get_device_properties(device).total_memory
+        torch.cuda.set_per_process_memory_fraction(min(1.0, a.max_cuda_gib * 2**30 / total), device)
+    torch.manual_seed(a.seed)
+    rng = np.random.default_rng(a.seed)
+
+    bundle = SS.restore_standalone(a.checkpoint, device=device, weights="ema", conditioner_source="embedded")
+    diffusion, scaler = bundle.diffusion, bundle.scaler
+    ext = SS.load_conditions(a.condition_npz, bundle, default_cells_per_bulk=1)
+    folds = pd.read_csv(a.dataset_dir / "folds.csv")
+    if not np.array_equal(folds["sample_id"].astype(str).to_numpy(), ext.sample_ids.astype(str)):
+        raise SystemExit("condition NPZ rows differ from folds.csv")
+    support = None
+    if a.support_manifest is not None:
+        from support_manifest import select_rows
+        support = support_preflight
+        train_rows, test_rows = select_rows(support, folds)
+        inner_rows = np.zeros(0, dtype=np.int64)
+        selected_visits = set(folds.loc[folds["donor_id"].astype(str).isin(support["train_donor_ids"]),
+                                        "sample_id"].astype(str))
+        if selected_visits - set(support["train_sample_ids"]) != set(support["excluded_sample_ids"]):
+            raise SystemExit("support excludes inconsistent selected-donor visits")
+        folds = folds.assign(fold=np.where(folds.index.isin(test_rows), 0,
+                                           np.where(folds.index.isin(train_rows), 1, -1)),
+                             inner_val_custom=False)
+        a.fold = 0
+        inner_col = "inner_val_custom"
+    elif a.train_donors_file:
+        # few-pair deployment scenario: train on the listed donors only, no inner validation, and treat every other
+        # usable row (fold >= 0) as test; --fold is ignored
+        want = {l.strip() for l in Path(a.train_donors_file).read_text().splitlines() if l.strip()}
+        is_train = folds["donor_id"].astype(str).isin(want) & (folds["fold"] >= 0)
+        if int(folds.loc[is_train, "donor_id"].nunique()) != len(want):
+            raise SystemExit("some listed training donors are missing or excluded")
+        folds = folds.assign(fold=np.where(folds["fold"] < 0, -1, np.where(is_train, 1, 0)))
+        folds["inner_val_custom"] = False
+        a.fold = 0
+        inner_col = "inner_val_custom"
+    else:
+        inner_col = f"inner_val_f{a.fold}"
+    test_rows = folds.index[folds["fold"] == a.fold].to_numpy()
+    inner_rows = folds.index[folds[inner_col]].to_numpy()
+    # fold -1 marks rows excluded by the pairing identity checks: never trained on, never tested
+    train_rows = folds.index[(folds["fold"] != a.fold) & (folds["fold"] >= 0)
+                             & ~folds[inner_col]].to_numpy()
+    subset_info = None
+    if a.train_n:
+        # learning curve: keep a stratified, nested subset of the training donors (inner-validation and test
+        # rows unchanged).  Each stratum is permuted once by --subset-seed, so smaller subsets are prefixes of
+        # larger ones; allocation is proportional to stratum size with at least one donor per stratum.
+        grp = folds.loc[train_rows, "group"].astype(str)
+        strata = (grp.str.contains(a.strata_regex, regex=True) if a.strata_regex
+                  else pd.Series(True, index=grp.index)).to_numpy()
+        srng = np.random.default_rng(a.subset_seed)
+        labels = sorted(set(strata.tolist()))
+        perm = {s: train_rows[strata == s][srng.permutation(int((strata == s).sum()))] for s in labels}
+        n = min(int(a.train_n), len(train_rows))
+        sizes = {s: len(perm[s]) for s in labels}
+        alloc = {s: max(1, int(np.floor(n * sizes[s] / len(train_rows)))) for s in labels}
+        while sum(alloc.values()) < n:
+            s = max(labels, key=lambda s: n * sizes[s] / len(train_rows) - alloc[s])
+            alloc[s] += 1
+        while sum(alloc.values()) > n:
+            s = max((s for s in labels if alloc[s] > 1), key=lambda s: alloc[s])
+            alloc[s] -= 1
+        train_rows = np.sort(np.concatenate([perm[s][:alloc[s]] for s in labels]))
+        subset_info = {"train_n": int(n), "subset_seed": a.subset_seed, "strata_regex": a.strata_regex,
+                       "allocation": {str(k): int(v) for k, v in alloc.items()}}
+    if set(test_rows) & (set(inner_rows) | set(train_rows)):
+        raise SystemExit("test rows overlap training rows")
+    if set(folds.loc[test_rows, "donor_id"]) & set(folds.loc[np.r_[train_rows, inner_rows], "donor_id"]):
+        raise SystemExit("a test donor also appears in training")
+
+    cells = Cells(cells_path, bundle.gene_ids)
+    with np.load(cells_path, allow_pickle=False) as z:
+        panel_measured = z["panel_measured"].astype(bool)
+    if not np.array_equal(panel_measured, ext.output_availability_mask.astype(bool)):
+        raise SystemExit("cells' measured panel differs from the condition NPZ output_availability_mask")
+
+    base = torch.from_numpy(ext.base_conditions.astype(np.float32)).to(device)
+    bulk_frac = torch.from_numpy(ext.bulk_measurement_fraction.astype(np.float32)).to(device)
+    avail_rows = torch.from_numpy(ext.output_availability_mask.astype(bool)).to(device)
+    cond_rows = condition_with_measurement_context(base, bulk_frac, avail_rows)   # [rows, condition_dim]
+
+    denoiser = diffusion.denoiser
+    info = set_trainable(denoiser, a.mode, a.lora_rank, a.lora_alpha)
+    params = [p for p in denoiser.parameters() if p.requires_grad]
+    named = [(n, p) for n, p in denoiser.named_parameters() if p.requires_grad]
+    opt = torch.optim.AdamW(params, lr=a.lr, weight_decay=0.0)
+    ema = {n: p.detach().clone() for n, p in named}
+    log(f"mode={a.mode} trainable={info['n_trainable']:,} rows train/inner/test="
+        f"{len(train_rows)}/{len(inner_rows)}/{len(test_rows)} cells(train)={int(cells.count[train_rows].sum()):,}")
+
+    # fixed inner-validation draws
+    vrng = np.random.default_rng(a.seed + 1)
+    val_cells, val_rows = [], []
+    for r in inner_rows:
+        n = int(cells.count[r])
+        take = min(a.val_cells_per_row, n)
+        pick = cells.start[r] + np.sort(vrng.choice(n, size=take, replace=False))
+        val_cells.append(pick)
+        val_rows.append(np.full(take, r))
+    val_cells = np.concatenate(val_cells) if val_cells else np.zeros(0, np.int64)
+    val_rows = np.concatenate(val_rows) if val_rows else np.zeros(0, np.int64)
+
+    def to_batch(r: np.ndarray, c: np.ndarray):
+        x_log = torch.from_numpy(np.log1p(cells.dense(c))).to(device)
+        rr = torch.from_numpy(r).to(device)
+        availability = avail_rows[rr]
+        return scaler.to_unit_tensor(x_log), cond_rows[rr], availability, x_log > 0
+
+    def swap_in_ema() -> dict:
+        backup = {n: p.detach().clone() for n, p in named}
+        with torch.no_grad():
+            for n, p in named:
+                p.copy_(ema[n])
+        return backup
+
+    def restore(backup: dict) -> None:
+        with torch.no_grad():
+            for n, p in named:
+                p.copy_(backup[n])
+
+    @torch.no_grad()
+    def val_loss() -> float:
+        if len(val_cells) == 0:
+            return float("nan")
+        diffusion.eval()
+        tot, n = 0.0, 0
+        for rep in range(a.val_repeats):
+            for i in range(0, len(val_cells), a.batch_size):
+                g = torch.Generator(device=device).manual_seed(a.seed + 1_000_003 * rep + i)
+                v, c, m, act = to_batch(val_rows[i:i + a.batch_size], val_cells[i:i + a.batch_size])
+                with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=device.type == "cuda"):
+                    res = diffusion.training_loss(v, c, availability_mask=m, active_target=act, generator=g)
+                tot += float(res.loss) * v.shape[0]
+                n += v.shape[0]
+        diffusion.train()
+        return tot / max(n, 1)
+
+    history = []
+    best = {"loss": val_loss(), "step": 0}
+    best_state = {n: t.detach().cpu().clone() for n, t in ema.items()}
+    history.append({"step": 0, "inner_val_loss": best["loss"], "train_loss": None})
+    log(f"step 0 inner-val loss {best['loss']:.5f} (pretrained)")
+    gen = torch.Generator(device=device).manual_seed(a.seed + 7)
+    diffusion.train()
+    t0 = time.time()
+    run_loss, run_n, bad = 0.0, 0, 0
+    max_steps = 20 if a.smoke else (a.fixed_steps or a.max_steps)
+    eval_every = 5 if a.smoke else a.eval_every
+    step = 0
+    for step in range(1, max_steps + 1):
+        for gparam in opt.param_groups:
+            gparam["lr"] = a.lr * min(1.0, step / max(1, a.warmup))
+        r, c = draw_batch(cells, train_rows, a.batch_size, rng)
+        v, cond, m, act = to_batch(r, c)
+        opt.zero_grad(set_to_none=True)
+        with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=device.type == "cuda"):
+            res = diffusion.training_loss(v, cond, availability_mask=m, active_target=act, generator=gen)
+        if not bool(torch.isfinite(res.loss)):
+            raise FloatingPointError(f"non-finite loss at step {step}")
+        res.loss.backward()
+        norm = nn.utils.clip_grad_norm_(params, 5.0)
+        if not bool(torch.isfinite(norm)):
+            raise FloatingPointError(f"non-finite grad norm at step {step}")
+        opt.step()
+        with torch.no_grad():
+            for n_, p in named:
+                ema[n_].lerp_(p.detach(), 1.0 - a.ema_decay)
+        run_loss += float(res.loss.detach())
+        run_n += 1
+        if a.fixed_steps and step == max_steps:
+            best = {"loss": float("nan"), "step": step}
+            best_state = {n_: t.detach().cpu().clone() for n_, t in ema.items()}
+        if step % eval_every == 0 and not a.fixed_steps:
+            backup = swap_in_ema()
+            vl = val_loss()
+            restore(backup)
+            rec = {"step": step, "inner_val_loss": vl, "train_loss": run_loss / run_n,
+                   "sec": round(time.time() - t0, 1)}
+            history.append(rec)
+            run_loss, run_n = 0.0, 0
+            if vl < best["loss"] - 1e-6:
+                best = {"loss": vl, "step": step}
+                best_state = {n_: t.detach().cpu().clone() for n_, t in ema.items()}
+                bad = 0
+            else:
+                bad += 1
+            log(f"step {step} train {rec['train_loss']:.5f} inner-val {vl:.5f} best {best['loss']:.5f}@{best['step']} "
+                f"({rec['sec']:.0f}s)")
+            (a.out_dir / "history.json").write_text(json.dumps(history, indent=1))
+            if bad >= a.patience:
+                log(f"early stop: no improvement in {a.patience} evaluations")
+                break
+
+    # load the selected EMA state, merge LoRA, save a self-contained checkpoint
+    with torch.no_grad():
+        for n_, p in named:
+            p.copy_(best_state[n_].to(device))
+    if a.mode == "lora":
+        merge_lora(denoiser)
+    state = {k: v.detach().cpu().clone() for k, v in denoiser.state_dict().items()}
+    payload_in = SS._load_checkpoint_cpu(a.checkpoint)
+    ref_state = payload_in["ema_state"]["model_state"]
+    if set(ref_state.keys()) != set(state.keys()):
+        raise SystemExit("fine-tuned state keys differ from the checkpoint's")
+    changed = {k: float((state[k].float() - ref_state[k].float()).abs().max()) for k in state}
+    out = {k: payload_in[k] for k in KEEP_KEYS if k in payload_in}
+    out["model_state"] = state
+    out["ema_state"] = {"model_state": state}
+    ft_meta = {
+        "schema": SCHEMA, "source_checkpoint": str(a.checkpoint), "source_checkpoint_sha256": got,
+        "dataset_dir": str(a.dataset_dir), "condition_npz": str(a.condition_npz),
+        "condition_npz_sha256": ext.input_sha256, "fold": a.fold, "mode": a.mode, "trainable": info,
+        "lr": a.lr, "warmup": a.warmup, "batch_size": a.batch_size, "ema_decay": a.ema_decay,
+        "max_steps": max_steps, "steps_run": step, "eval_every": eval_every, "patience": a.patience,
+        "train_subset": subset_info, "train_donors_file": a.train_donors_file or None,
+        "support_manifest": str(a.support_manifest) if support else None,
+        "support_sha256": support["sha256"] if support else None,
+        "train_donor_ids": support["train_donor_ids"] if support else None,
+        "excluded_selected_donor_visits": support["excluded_sample_ids"] if support else None,
+        "fixed_steps": a.fixed_steps or None,
+        "selected_step": best["step"], "selected_inner_val_loss": best["loss"],
+        "pretrained_inner_val_loss": history[0]["inner_val_loss"],
+        "train_rows": [str(x) for x in folds.loc[train_rows, "sample_id"]],
+        "inner_val_rows": [str(x) for x in folds.loc[inner_rows, "sample_id"]],
+        "test_rows_never_loaded_for_training": [str(x) for x in folds.loc[test_rows, "sample_id"]],
+        "max_abs_weight_change": max(changed.values()),
+        "n_tensors_changed": int(sum(v > 0 for v in changed.values())),
+        "seed": a.seed, "wall_seconds": round(time.time() - t0, 1),
+        "written_utc": datetime.now(timezone.utc).isoformat(),
+    }
+    if isolation is not None:
+        ft_meta["support_expression_isolation"] = isolation
+    out["finetune"] = ft_meta
+    tmp = a.out_dir / "checkpoint_ft.pt.tmp"
+    torch.save(out, tmp)
+    os.replace(tmp, done)
+    ft_meta["checkpoint_sha256"] = sha256_file(done)
+    # reload through the sampler's own restore path and compare one forward pass
+    check = SS.restore_standalone(done, device=torch.device("cpu"), weights="ema", conditioner_source="embedded")
+    ref = SS.restore_standalone(done, device=torch.device("cpu"), weights="raw", conditioner_source="embedded")
+    xs = torch.randn(4, check.diffusion.state_dim, generator=torch.Generator().manual_seed(0))
+    tt = torch.full((4,), 0.5)
+    cc = cond_rows[torch.from_numpy(train_rows[:1]).to(device)].repeat(4, 1).float().cpu()
+    denoiser_cpu = denoiser.to("cpu").float().eval()
+    with torch.no_grad():
+        o1 = check.diffusion.denoiser(xs, tt, cc).value_prediction
+        o2 = denoiser_cpu(xs, tt, cc).value_prediction
+        o3 = ref.diffusion.denoiser(xs, tt, cc).value_prediction
+    ft_meta["reload_forward_max_abs_diff"] = float(max((o1 - o2).abs().max(), (o1 - o3).abs().max()))
+    if ft_meta["reload_forward_max_abs_diff"] > 1e-4:
+        raise SystemExit(f"reloaded checkpoint differs: {ft_meta['reload_forward_max_abs_diff']}")
+    (a.out_dir / "history.json").write_text(json.dumps(history, indent=1))
+    (a.out_dir / "RUN.json").write_text(json.dumps(ft_meta, indent=1))
+    log(f"saved {done} (selected step {best['step']}, inner-val {best['loss']:.5f} vs pretrained "
+        f"{history[0]['inner_val_loss']:.5f}); sha {ft_meta['checkpoint_sha256'][:12]}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

paper-releases/pbisc-diffusion/updates/20261012T143729Z-strictpairs-recovery-two-host/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T143729Z-strictpairs-recovery-two-host/snapshot/17/prepare_draw_inputs.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python
+"""Build scSemiProfiler inputs for one paired split (CPU only).
+
+Representatives = every usable row of the split's training donors (paired sc + real bulk).
+Targets        = the split's test rows (real bulk only; their cells are never read here).
+
+  --mode fewshot --draw j : training donors = data/<ds>/fewshot_n5/draw<j>/train_donors.txt,
+                            targets = draw<j>/test_samples.txt, condition = draw<j>/test_raw.npz
+  --mode cv --fold k      : training donors = every usable donor not in fold k (inner-val donors included),
+                            targets = fold k's rows, condition = data/<ds>/fold<k>/test_raw.npz
+  --mode budget --fold k --n N   (added 2026-10-02): representatives = exactly the rows LoRA lora_n<N> of fold k saw,
+                            runs/<ds>/fold<k>/lora_n<N>/RUN.json train_rows + inner_val_rows (checked against
+                            data/<ds>/CAL_BUDGET.json fit_sample_ids when present); targets = fold k's rows (checked
+                            against the run's test_rows_never_loaded_for_training); condition = fold<k>/test_raw.npz.
+                            The manifest also records target_donors (used by run_scsemi_paired.py --reuse-from).
+  --check-only            (added 2026-10-02): build and check everything (split, genes, cells, depth, condition), write
+                            nothing under --work; print the summary (and the representative / target lists to
+                            --check-out if given).  Without it the builder behaves exactly as before.
+
+Modeled genes = final 6k panel genes measured in the bulk of every representative and target row AND in the
+single cells of every representative row (features.npz bulk_valid / sc_valid).  No target single-cell mask,
+depth or cell count enters.
+
+Writes <work>/inputs/:
+  bulk.h5ad        raw bulk counts (float64) of reps + targets on the modeled genes, obs['sample_ids'],
+                   rows in features.npz order; upstream initsetup(normed=False) library-normalises + log1p's it
+  rep_sc.h5ad      raw UMI counts (float32 CSR) of every representative cell on the modeled genes,
+                   obs['sample_ids'] = the representative row's sample id; upstream scprocess(normed=False)
+  inputs_manifest.json   rows, genes, depth rule, source hashes
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+if os.environ.get("CUDA_VISIBLE_DEVICES") != "":
+    raise SystemExit("run with CUDA_VISIBLE_DEVICES=''")
+
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+
+TASK = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927")
+HERE = TASK / "scsemi"
+sys.path.insert(0, str(TASK / "tools"))
+import prepare_dataset as PD  # noqa: E402  (the benchmark's own loader: bulk_raw with unmeasured NaN -> 0, masks, use)
+
+SEED = 20260927
+
+
+def sha(path: Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(1 << 24), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def split_rows(ds: str, mode: str, j: int, sample_ids: np.ndarray, folds: pd.DataFrame,
+               n: int | None = None, support_manifest: Path | None = None) -> dict:
+    data = TASK / "data" / ds
+    usable = folds["fold"].to_numpy() >= 0
+    donors = folds["donor_id"].astype(str).to_numpy()
+    extra = {}
+    if support_manifest is not None:
+        strict_tools = TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/tools"
+        sys.path.insert(0, str(strict_tools))
+        from support_manifest import load_manifest, select_rows
+        m = load_manifest(support_manifest, folds)
+        if m["cohort"] != ds:
+            raise SystemExit("support cohort differs from dataset")
+        rep, tgt = select_rows(m, folds)
+        rel = support_manifest.resolve().parent.relative_to(
+            TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/protocol/splits")
+        cond = TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/data" / rel / "test_raw.npz"
+        return {"rep": rep, "tgt": tgt, "train_donors": m["train_donor_ids"],
+                "test": m["test_sample_ids"], "condition": cond,
+                "sources": {"support.json": support_manifest},
+                "support_sha256": m["sha256"], "target_donors": m["test_donor_ids"]}
+    if mode == "fewshot":
+        dd = data / "fewshot_n5" / f"draw{j}"
+        train_donors = [x.strip() for x in (dd / "train_donors.txt").read_text().splitlines() if x.strip()]
+        test = [x.strip() for x in (dd / "test_samples.txt").read_text().splitlines() if x.strip()]
+        cond = dd / "test_raw.npz"
+        sources = {"train_donors.txt": dd / "train_donors.txt", "test_samples.txt": dd / "test_samples.txt"}
+        rep = np.flatnonzero(np.isin(donors, train_donors) & usable)
+    elif mode == "cv":
+        fold = folds["fold"].to_numpy()
+        test = sample_ids[fold == j].tolist()
+        rep = np.flatnonzero(usable & (fold != j))
+        train_donors = sorted(set(donors[rep]))
+        cond = data / f"fold{j}" / "test_raw.npz"
+        sources = {}
+    elif mode == "budget":
+        fold = folds["fold"].to_numpy()
+        rj = TASK / "runs" / ds / f"fold{j}" / f"lora_n{n}" / "RUN.json"
+        run = json.loads(rj.read_text())
+        if run["fold"] != j or (run.get("train_subset") or {}).get("train_n") != n:
+            raise SystemExit(f"{rj}: not the fold {j} n={n} learning-curve run")
+        fit = list(run["train_rows"]) + list(run["inner_val_rows"])
+        test = sample_ids[fold == j].tolist()
+        if sorted(run["test_rows_never_loaded_for_training"]) != sorted(test):
+            raise SystemExit(f"{rj}: its test rows differ from fold {j}'s rows")
+        pos = {s: i for i, s in enumerate(sample_ids)}
+        if len(set(fit)) != len(fit) or any(s not in pos for s in fit):
+            raise SystemExit(f"{rj}: duplicate or unknown fit rows")
+        rep = np.asarray(sorted(pos[s] for s in fit), dtype=np.int64)
+        if not (usable[rep] & (fold[rep] != j)).all():
+            raise SystemExit(f"{rj}: a fit row is unusable or in fold {j}")
+        cb = data / "CAL_BUDGET.json"
+        if cb.exists():
+            e = json.loads(cb.read_text())["folds"][str(j)]["n"][str(n)]
+            if sorted(e["fit_sample_ids"]) != sorted(fit) or e["lora_checkpoint_sha256"] != run["checkpoint_sha256"]:
+                raise SystemExit(f"{cb}: fold {j} n={n} fit rows / LoRA checkpoint differ from {rj}")
+        train_donors = sorted(set(donors[rep]))
+        cond = data / f"fold{j}" / "test_raw.npz"
+        sources = {"lora_RUN.json": rj, **({"CAL_BUDGET.json": cb} if cb.exists() else {})}
+        extra = {"budget": {"n": int(n), "fold": int(j), "lora_run": str(rj), "lora_checkpoint_sha256": run["checkpoint_sha256"],
+                            "n_train_rows": len(run["train_rows"]), "n_inner_val_rows": len(run["inner_val_rows"]),
+                            "train_rows": list(run["train_rows"]), "inner_val_rows": list(run["inner_val_rows"])}}
+    else:
+        raise SystemExit(f"unknown mode {mode}")
+    pos = {s: i for i, s in enumerate(sample_ids)}
+    miss = [s for s in test if s not in pos]
+    if miss:
+        raise SystemExit(f"test samples not in features.npz: {miss[:5]}")
+    tgt = np.asarray([pos[s] for s in test], dtype=np.int64)
+    if not usable[tgt].all():
+        raise SystemExit("a test row is not usable (fold -1)")
+    if set(donors[rep]) & set(donors[tgt]):
+        raise SystemExit("a donor is both representative and target")
+    if len(set(test)) != len(test):
+        raise SystemExit("duplicate test samples")
+    return {"rep": rep, "tgt": tgt, "train_donors": train_donors, "test": test, "condition": cond, "sources": sources,
+            **extra}
+
+
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--dataset", required=True)
+    ap.add_argument("--mode", choices=["fewshot", "cv", "budget"], default="fewshot")
+    ap.add_argument("--draw", type=int, help="fewshot draw index")
+    ap.add_argument("--fold", type=int, help="cv / budget fold index")
+    ap.add_argument("--n", type=int, help="budget: the LoRA learning-curve n (runs/<ds>/fold<k>/lora_n<n>)")
+    ap.add_argument("--work", type=Path, required=True)
+    ap.add_argument("--smoke-reps", type=int, default=0, help="SMOKE ONLY: keep the first N representative rows")
+    ap.add_argument("--smoke-cells", type=int, default=0, help="SMOKE ONLY: at most N cells per representative")
+    ap.add_argument("--smoke-targets", default="", help="SMOKE ONLY: comma list of target sample ids")
+    ap.add_argument("--smoke-rep-ids", default="", help="SMOKE ONLY: comma list of representative sample ids to keep")
+    ap.add_argument("--check-only", action="store_true", help="dry run: check everything, write nothing under --work")
+    ap.add_argument("--check-out", type=Path, default=None, help="--check-only: write the split summary JSON here")
+    ap.add_argument("--support-manifest", type=Path, default=None,
+                    help="strict support.json; representatives are its exact n sample IDs")
+    a = ap.parse_args(argv)
+    if a.support_manifest is not None:
+        if a.draw is not None or a.fold is not None or a.n is not None:
+            raise SystemExit("--support-manifest cannot be combined with --draw/--fold/--n")
+        a.mode = "strict"
+        j = 0
+    else:
+        j = a.draw if a.mode == "fewshot" else a.fold
+    if j is None:
+        raise SystemExit("--draw (fewshot) or --fold (cv, budget) required")
+    if (a.mode == "budget") != (a.n is not None):
+        raise SystemExit("--n is required with --mode budget and only valid there")
+    smoke = bool(a.smoke_reps or a.smoke_cells or a.smoke_targets or a.smoke_rep_ids)
+    if a.support_manifest is not None and smoke:
+        raise SystemExit("strict support forbids smoke subsetting")
+    work = a.work.resolve()
+    if smoke and "/scsemi/smoke/" not in str(work) + "/":
+        raise SystemExit("smoke subsetting only under scsemi/smoke/")
+    out = work / "inputs"
+    if not a.check_only:
+        if (out / "inputs_manifest.json").exists():
+            raise SystemExit(f"{out}/inputs_manifest.json exists; inputs are immutable (preserve and inspect)")
+        out.mkdir(parents=True, exist_ok=True)
+    data = TASK / "data" / a.dataset
+    isolation = None
+    if a.support_manifest is not None:
+        strict_tools = TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/tools"
+        sys.path.insert(0, str(strict_tools))
+        from support_input_isolation import bulk_only_load, materialize
+        d = bulk_only_load(data)
+    else:
+        d = PD.load(data)
+    folds = pd.read_csv(data / "folds.csv")
+    if not np.array_equal(folds["sample_id"].astype(str).to_numpy(), d["sample_ids"]):
+        raise SystemExit("folds.csv rows differ from features.npz")
+    s = split_rows(a.dataset, a.mode, j, d["sample_ids"], folds, n=a.n,
+                   support_manifest=a.support_manifest)
+    rep, tgt = s["rep"], s["tgt"]
+    if a.smoke_rep_ids:
+        keep_r = set(a.smoke_rep_ids.split(","))
+        rep = np.asarray([r for r in rep if d["sample_ids"][r] in keep_r], dtype=np.int64)
+    if a.smoke_reps:
+        rep = rep[: a.smoke_reps]
+    if a.smoke_targets:
+        keep = set(a.smoke_targets.split(","))
+        tgt = np.asarray([t for t in tgt if d["sample_ids"][t] in keep], dtype=np.int64)
+        s["test"] = [x for x in s["test"] if x in keep]
+    if a.mode == "fewshot" and not smoke:
+        usable = np.flatnonzero(folds["fold"].to_numpy() >= 0)
+        if set(rep.tolist()) | set(tgt.tolist()) != set(usable.tolist()):
+            raise SystemExit("representatives + targets do not cover the usable rows")
+    # modeled genes
+    cells_path = data / "cells_panel6k.npz"
+    if a.support_manifest is not None:
+        cells_path, isolation = materialize(a.support_manifest, dataset_dir=data)
+    z = np.load(cells_path, allow_pickle=False)
+    panel = z["panel_gene_ids"].astype(str)
+    gpos = {g: i for i, g in enumerate(d["gene_ids"])}
+    pcanon = np.asarray([gpos[g] for g in panel], dtype=np.int64)
+    rows = np.sort(np.concatenate([rep, tgt]))
+    bulk_ok = d["bulk_valid"][rows][:, pcanon].all(0)
+    sc_ok = d["sc_valid"][rep][:, pcanon].all(0)
+    model_pidx = np.flatnonzero(bulk_ok & sc_ok)
+    genes = panel[model_pidx]
+    if genes.size < 1000:
+        raise SystemExit(f"only {genes.size} modeled genes")
+    # scorer's scoring genes (all rows of the dataset) for the record
+    score_mask = d["bulk_valid"][:, pcanon].all(0) & d["sc_valid"][:, pcanon].all(0)
+    # bulk
+    import anndata as ad
+    B = d["bulk"][rows][:, pcanon[model_pidx]].astype(np.float64)
+    if not np.isfinite(B).all() or (B < 0).any():
+        raise SystemExit("non-finite or negative bulk on the modeled genes")
+    if (B.sum(1) <= 0).any():
+        raise SystemExit("a bulk row has no counts on the modeled genes")
+    sym = pd.read_csv(TASK.parents[1] / "artifacts/panel_ladder_floor01_1m_20260923/tiers/6k/selected_genes.csv")
+    sym = sym.set_index("gene_id").gene_symbol.astype(str)
+    var = pd.DataFrame({"gene_symbol": sym.reindex(genes).to_numpy()}, index=pd.Index(genes, name="gene_id"))
+    obs_b = pd.DataFrame({"sample_ids": d["sample_ids"][rows], "donor_id": d["donor_ids"][rows],
+                          "group": d["groups"][rows], "role": np.where(np.isin(rows, rep), "representative", "target")},
+                         index=pd.Index(d["sample_ids"][rows], name="obs"))
+    if not a.check_only:
+        ad.AnnData(X=B, obs=obs_b, var=var).write_h5ad(out / "bulk.h5ad")
+    # representative cells
+    X = sp.csr_matrix((z["data"], z["indices"], z["indptr"]), shape=tuple(int(x) for x in z["shape"]))
+    rs = np.asarray(z["row_sample"], dtype=np.int64)
+    z.close()
+    blocks, obs_rows, per_rep = [], [], []
+    rng = np.random.default_rng(SEED)
+    for r in rep:
+        cells = np.flatnonzero(rs == r)
+        if a.smoke_cells and cells.size > a.smoke_cells:
+            cells = np.sort(rng.choice(cells, a.smoke_cells, replace=False))
+        Xr = X[cells][:, model_pidx].tocsr().astype(np.float32)
+        lib = np.asarray(Xr.sum(1)).ravel()
+        if (lib <= 0).any():
+            raise SystemExit(f"{d['sample_ids'][r]}: {int((lib <= 0).sum())} cells without counts on the modeled genes")
+        blocks.append(Xr)
+        obs_rows.append(pd.DataFrame({"sample_ids": d["sample_ids"][r], "donor_id": d["donor_ids"][r],
+                                      "source_cell_row": cells}))
+        per_rep.append({"sample_id": str(d["sample_ids"][r]), "donor_id": str(d["donor_ids"][r]),
+                        "group": str(d["groups"][r]), "n_cells": int(cells.size),
+                        "median_umi_modeled_genes": float(np.median(lib))})
+    Xs = sp.vstack(blocks, format="csr")
+    obs_s = pd.concat(obs_rows, ignore_index=True)
+    obs_s.index = pd.Index([f"{a_}|{b_}" for a_, b_ in zip(obs_s.sample_ids, obs_s.source_cell_row)], name="cell")
+    if not a.check_only:
+        ad.AnnData(X=Xs, obs=obs_s, var=var.copy()).write_h5ad(out / "rep_sc.h5ad")
+    lib_all = np.asarray(Xs.sum(1)).ravel()
+    depth = float(np.median(lib_all))
+    cond = s["condition"]
+    man = {
+        "schema": "scsemi_paired.inputs.v1", "written_utc": datetime.now(timezone.utc).isoformat(),
+        "dataset": a.dataset, "mode": a.mode, "split": j, "smoke": smoke,
+        "smoke_settings": {"reps": a.smoke_reps, "cells": a.smoke_cells, "targets": a.smoke_targets,
+                           "rep_ids": a.smoke_rep_ids} if smoke else None,
+        "train_donors": s["train_donors"],
+        "representatives": per_rep, "targets": [str(x) for x in d["sample_ids"][tgt]],
+        "target_order_test_samples": s["test"],
+        "bulk_row_order": [str(x) for x in d["sample_ids"][rows]],
+        "n_representatives": int(rep.size), "n_targets": int(tgt.size), "n_rep_cells": int(Xs.shape[0]),
+        "modeled_genes": {"n": int(genes.size), "gene_ids": genes.tolist(), "panel_index": model_pidx.tolist(),
+                          "rule": "6k panel genes with bulk_valid in every rep+target row and sc_valid in every rep row",
+                          "equals_scorer_scoring_genes": bool(np.array_equal(model_pidx, np.flatnonzero(score_mask))),
+                          "n_scorer_scoring_genes": int(score_mask.sum())},
+        "depth": {"value": depth, "rule": "median over all representative cells (all rows of the training donors) of the "
+                  "per-cell UMI sum on the modeled genes; every generated cell is scaled to this expected library",
+                  "n_cells": int(lib_all.size)},
+        "condition_npz": str(cond), "condition_sha256": sha(cond),
+        "sources": {"features.npz": sha(data / "features.npz"), "folds.csv": sha(data / "folds.csv"),
+                    "cells_panel6k.npz": sha(data / "cells_panel6k.npz"),
+                    **{k: sha(v) for k, v in s["sources"].items()}},
+        "files": ({"bulk.h5ad": sha(out / "bulk.h5ad"), "rep_sc.h5ad": sha(out / "rep_sc.h5ad")}
+                  if not a.check_only else None),
+        "builder_sha256": sha(Path(__file__)),
+    }
+    if a.mode == "strict":
+        man["support_sha256"] = s["support_sha256"]
+        man["target_donors"] = s["target_donors"]
+        man["support_expression_isolation"] = isolation
+        man["sources"]["cells_support_only.npz"] = isolation["isolated_cells_sha256"]
+        man["sources"]["support_input_isolation.py"] = isolation["builder_sha256"]
+        man["depth"]["rule"] = ("median over cells of the exact support samples of the per-cell UMI sum "
+                                "on the modeled genes; every generated cell is scaled to this expected library")
+    if a.mode == "budget":
+        man["target_donors"] = sorted({str(x) for x in d["donor_ids"][tgt]})
+        man["budget"] = s["budget"]
+    with np.load(cond, allow_pickle=False) as cz:
+        cond_ids = cz["sample_ids"].astype(str).tolist()
+    if not smoke and sorted(cond_ids) != sorted(man["targets"]):
+        raise SystemExit("condition NPZ sample_ids differ from the targets")
+    man["condition_sample_order"] = cond_ids
+    if a.check_only:
+        summary = {"dataset": a.dataset, "mode": a.mode, "split": j, "n": a.n, "work": str(work),
+                   "reps": [r["sample_id"] for r in per_rep], "targets": man["targets"],
+                   "rep_cells": {r["sample_id"]: r["n_cells"] for r in per_rep}, "n_rep_cells": int(Xs.shape[0]),
+                   "modeled_genes": int(genes.size), "modeled_panel_index": model_pidx.tolist(),
+                   "equals_scorer_genes": man["modeled_genes"]["equals_scorer_scoring_genes"],
+                   "depth": depth, "condition_npz": str(cond), "condition_matches_targets": True,
+                   "train_donors": s["train_donors"], **({"budget": s["budget"]} if a.mode == "budget" else {})}
+        if a.check_out is not None:
+            a.check_out.parent.mkdir(parents=True, exist_ok=True)
+            a.check_out.write_text(json.dumps(summary, indent=1) + "\n")
+        print(json.dumps({k: summary[k] for k in ("dataset", "mode", "split", "n", "n_rep_cells", "modeled_genes",
+                                                  "equals_scorer_genes", "depth")} | {"reps": len(summary["reps"]),
+                                                                                       "targets": len(summary["targets"])}))
+        return 0
+    tmp = out / "inputs_manifest.json.tmp"
+    tmp.write_text(json.dumps(man, indent=1) + "\n")
+    tmp.replace(out / "inputs_manifest.json")
+    print(json.dumps({"dataset": a.dataset, "mode": a.mode, "split": j, "reps": int(rep.size), "targets": int(tgt.size),
+                      "rep_cells": int(Xs.shape[0]), "modeled_genes": int(genes.size),
+                      "equals_scorer_genes": man["modeled_genes"]["equals_scorer_scoring_genes"], "depth": depth}))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

paper-releases/pbisc-diffusion/updates/20261012T143729Z-strictpairs-recovery-two-host/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T143729Z-strictpairs-recovery-two-host/snapshot/18/immicro-healthy-fold1-n5-train-proof.json

@@ -0,0 +1,39 @@
+{
+  "operation": "immicro-healthy-fold1-n5-train",
+  "old_signature_reproduced": "1f82de5256dadba1d66d0756684eac3d4373329abe9606684ebb9036cd42618f",
+  "new_signature": "6b102fb7edbdafd80b61c231398bf40bfbf6bec4420c148a9e265b1064a45263",
+  "source_backups": [
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+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paper_figures_strictpairs_20261012/tools/support_input_isolation.py",
+      "sha256": "1e383c79085c6d20afd839b401ec2e321df3d535d3d1b5552ba9f0e59ba033af"
+    },
+    {
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_sdd.py",
+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paired_finetune_intradataset_20260927/tools/ft_sdd.py",
+      "sha256": "e7f3d38b65932ffe99292a8841a15eb9e7cce205afec1de6b34b59a06731b82d"
+    },
+    {
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/tools/prepare_draw_inputs.py",
+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paired_finetune_intradataset_20260927/scsemi/tools/prepare_draw_inputs.py",
+      "sha256": "b11b9f206a04fdac99dc489a8387647f0a334a59d5761a0c1bd964301933da8f"
+    }
+  ],
+  "support_manifest_sha256": "0c8f07d519c3220b15f67ad58975b8f4938556d12da65c1d9e15a6625cc4422d",
+  "selected_computation_replay": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/isolated/immicro/healthy/fold1/n5/REPLAY.json",
+  "outputs_unchanged": [
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold1/n5/lora/checkpoint_ft.pt",
+      "bytes": 217979993,
+      "mtime_ns": 1791811321684029262,
+      "sha256": "b3307fb321381403e5b584e9b458dc26bb150e507193331cd51938a40333c0be"
+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold1/n5/lora/RUN.json",
+      "bytes": 2971,
+      "mtime_ns": 1791811324045528145,
+      "sha256": "9cb350164d0cfa7ca254754ba0149a2b8b45fbb4ce87c363e5669171dd8393a6"
+    }
+  ],
+  "limitation": "Original loaders materialized non-support arrays before selection. Reuse establishes selected-computation equivalence, not historical physical input isolation."
+}

paper-releases/pbisc-diffusion/updates/20261012T143729Z-strictpairs-recovery-two-host/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T143729Z-strictpairs-recovery-two-host/snapshot/19/immicro-healthy-fold0-n5-train-proof.json

@@ -0,0 +1,39 @@
+{
+  "operation": "immicro-healthy-fold0-n5-train",
+  "old_signature_reproduced": "2ca886713c69144fd1ef558468dbeba98e7f9840c0fe183f683af69e065b14e2",
+  "new_signature": "ab97afdd574f8ec532dfade4e716e9ddfccf68dc732848f35932a6f8c15561d8",
+  "source_backups": [
+    {
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/tools/support_input_isolation.py",
+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paper_figures_strictpairs_20261012/tools/support_input_isolation.py",
+      "sha256": "1e383c79085c6d20afd839b401ec2e321df3d535d3d1b5552ba9f0e59ba033af"
+    },
+    {
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_sdd.py",
+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paired_finetune_intradataset_20260927/tools/ft_sdd.py",
+      "sha256": "e7f3d38b65932ffe99292a8841a15eb9e7cce205afec1de6b34b59a06731b82d"
+    },
+    {
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/tools/prepare_draw_inputs.py",
+      "backup": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/backups/recovery_input_isolation_20261012T142024Z/plans/paired_finetune_intradataset_20260927/scsemi/tools/prepare_draw_inputs.py",
+      "sha256": "b11b9f206a04fdac99dc489a8387647f0a334a59d5761a0c1bd964301933da8f"
+    }
+  ],
+  "support_manifest_sha256": "a8ead926beae1f969e4cdf4b4a555a110e37d0bf3a27be3f9e4720c879f88740",
+  "selected_computation_replay": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/isolated/immicro/healthy/fold0/n5/REPLAY.json",
+  "outputs_unchanged": [
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold0/n5/lora/checkpoint_ft.pt",
+      "bytes": 217979993,
+      "mtime_ns": 1791802695247754575,
+      "sha256": "51eb269bf13e9ead76da3fdd3bb9ca6400c2de7014f85286ecc0e0c11ad9df11"
+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold0/n5/lora/RUN.json",
+      "bytes": 2970,
+      "mtime_ns": 1791802697688986618,
+      "sha256": "92452f8776859a729274f5afc2e785a7d5869e80123a8297a5a3ac48719cc980"
+    }
+  ],
+  "limitation": "Original loaders materialized non-support arrays before selection. Reuse establishes selected-computation equivalence, not historical physical input isolation."
+}