GRN/grn_ccfm/scripts/precompute_sparse_attn.py

"""
Precompute per-cell sparse attention matrices (per-row top-K) to HDF5.

Instead of storing attn @ gene_emb (dense 512-dim), this stores the raw sparse
attention values with per-row top-K=300 sparsification. This preserves the
sparse GRN signal and enables consistent gene-pair attention across cells.

Output HDF5 layout:
    /attn_values        (N, G_full, K) float16  — top-K attention values per row
    /attn_indices       (N, G_full, K) int16    — column indices in G_full space
    /cell_names         (N,) string
    /valid_gene_mask    (G_full,) bool           — True = gene in scGPT vocab
    /pca_basis          (G_full, d) float32      — PCA projection basis from delta attn
    /pca_explained_var  (d,) float32             — explained variance per component
    /delta_mean         (G_full,) float32        — per-gene delta L2 norm mean
    /delta_std          (G_full,) float32         — per-gene delta L2 norm std
"""

import sys
import os
import argparse

# Set up paths
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
_PROJECT_ROOT = os.path.dirname(_SCRIPT_DIR)
sys.path.insert(0, _PROJECT_ROOT)

# Bootstrap scDFM
import _bootstrap_scdfm  # noqa: F401

import numpy as np
import torch
import h5py
from tqdm import tqdm
from scipy import sparse as sp
from sklearn.decomposition import PCA

from src.data.data import get_data_classes
from src.data.scgpt_extractor import FrozenScGPTExtractor


_REPO_ROOT = os.path.normpath(os.path.join(_PROJECT_ROOT, "..", "..", "transfer", "code"))


def extract_sparse_attn(
    extractor: FrozenScGPTExtractor,
    expression_batch: torch.Tensor,  # (B, G_full)
    top_k: int = 300,
    attn_layer: int = 11,
    use_rank_norm: bool = True,
) -> tuple:
    """
    Extract per-row top-K sparse attention for each cell.

    Returns:
        values: (B, G_full, K) float16 — top-K attention values (with sign)
        indices: (B, G_full, K) int16  — column indices in G_full space
    """
    B, G_full = expression_batch.shape
    device = expression_batch.device

    hvg_ids = extractor.hvg_to_scgpt_id  # (G_full,)
    valid_mask = hvg_ids >= 0
    valid_scgpt_ids = hvg_ids[valid_mask]  # (G_valid,)
    n_valid = valid_scgpt_ids.shape[0]
    valid_positions = torch.where(valid_mask)[0]  # (G_valid,) indices into G_full

    assert n_valid + 1 <= extractor.max_seq_len, (
        f"n_valid ({n_valid}) + 1 CLS > max_seq_len ({extractor.max_seq_len}). "
        f"Increase max_seq_len to at least {n_valid + 1}."
    )

    # Expression for valid genes
    expr_valid = expression_batch[:, valid_positions]  # (B, G_valid)

    # Build scGPT input: CLS + all valid gene tokens
    cls_ids = torch.full((B, 1), extractor.cls_token_id, dtype=torch.long, device=device)
    gene_ids_expanded = valid_scgpt_ids.unsqueeze(0).expand(B, -1)
    src = torch.cat([cls_ids, gene_ids_expanded], dim=1)  # (B, G_valid + 1)

    cls_val = torch.zeros(B, 1, device=device)
    values_in = torch.cat([cls_val, expr_valid], dim=1)  # (B, G_valid + 1)

    seq_len = n_valid + 1
    pad_mask = torch.zeros(B, seq_len, dtype=torch.bool, device=device)

    # Forward to target layer
    hidden = extractor._forward_to_layer(src, values_in, pad_mask, attn_layer)

    # Compute attention at target layer
    attn = extractor._compute_attention(hidden, attn_layer, use_rank_norm)  # (B, S, S)

    # Remove CLS row/column
    attn = attn[:, 1:, 1:]  # (B, G_valid, G_valid)

    # Per-row top-K in local (valid) space
    K = min(top_k, n_valid)
    _, topk_local_idx = attn.abs().topk(K, dim=-1)  # (B, G_valid, K)
    topk_vals = attn.gather(-1, topk_local_idx)      # (B, G_valid, K) — preserve sign

    # Map local indices → G_full space
    topk_full_idx = valid_positions[topk_local_idx]   # (B, G_valid, K)

    # Scatter valid gene rows into (B, G_full, K) output
    # Missing gene rows remain all-zero
    out_values = torch.zeros(B, G_full, K, device=device, dtype=torch.float32)
    out_indices = torch.zeros(B, G_full, K, device=device, dtype=torch.long)

    # valid_positions: (G_valid,) → expand for scatter
    vp = valid_positions.unsqueeze(0).unsqueeze(-1).expand(B, -1, K)  # (B, G_valid, K)
    out_values.scatter_(1, vp, topk_vals)
    out_indices.scatter_(1, vp, topk_full_idx)

    return out_values.half().cpu(), out_indices.short().cpu()


def compute_pca_basis(
    h5_values,      # HDF5 dataset (N, G_full, K) float16
    h5_indices,     # HDF5 dataset (N, G_full, K) int16
    cell_names,     # list of cell names
    adata,          # AnnData
    G_full: int,
    n_pairs: int = 1000,
    genes_per_pair: int = 50,
    max_components: int = 64,
    variance_threshold: float = 0.95,
    seed: int = 42,
):
    """
    Compute PCA basis from sampled sparse delta attention rows.

    Returns:
        pca_basis: (G_full, d) float32
        explained_var: (d,) float32
    """
    rng = np.random.RandomState(seed)
    name_to_idx = {name: i for i, name in enumerate(cell_names)}

    # Identify control/perturbation cells
    obs = adata.obs
    if "condition" in obs.columns:
        is_control = obs["condition"] == "control"
    elif "perturbation_covariates" in obs.columns:
        is_control = obs["perturbation_covariates"].str.contains("control", case=False)
    elif "treatment" in obs.columns:
        is_control = obs["treatment"] == "control"
    else:
        raise ValueError("Cannot identify control cells from adata.obs columns")

    ctrl_names = [n for n in obs.index[is_control] if n in name_to_idx]
    pert_names = [n for n in obs.index[~is_control] if n in name_to_idx]
    print(f"PCA basis: {len(ctrl_names)} control, {len(pert_names)} perturbation cells")

    n_pairs = min(n_pairs, len(ctrl_names), len(pert_names))
    ctrl_sample = rng.choice(ctrl_names, n_pairs, replace=True)
    pert_sample = rng.choice(pert_names, n_pairs, replace=True)

    # Collect sparse delta rows
    collected_rows = []
    delta_top = 30  # top entries to keep per row for delta

    for i in tqdm(range(n_pairs), desc="Sampling PCA rows"):
        ci = name_to_idx[ctrl_sample[i]]
        pi = name_to_idx[pert_sample[i]]

        # Load sparse attn for both cells: (G_full, K)
        ctrl_vals = h5_values[ci].astype(np.float32)   # (G_full, K)
        ctrl_idx = h5_indices[ci].astype(np.int32)      # (G_full, K)
        pert_vals = h5_values[pi].astype(np.float32)
        pert_idx = h5_indices[pi].astype(np.int32)

        # Pick random gene rows
        nonzero_rows = np.where(np.abs(ctrl_vals).sum(axis=1) > 0)[0]
        if len(nonzero_rows) < genes_per_pair:
            chosen = nonzero_rows
        else:
            chosen = rng.choice(nonzero_rows, genes_per_pair, replace=False)

        for g in chosen:
            # Merge two sparse vectors, compute delta, keep top-30
            # Build dense delta for this row
            delta_row = np.zeros(G_full, dtype=np.float32)

            # Pert sparse entries
            for k_i in range(pert_idx.shape[1]):
                col = pert_idx[g, k_i]
                if col >= 0:
                    delta_row[col] += pert_vals[g, k_i]

            # Ctrl sparse entries (subtract)
            for k_i in range(ctrl_idx.shape[1]):
                col = ctrl_idx[g, k_i]
                if col >= 0:
                    delta_row[col] -= ctrl_vals[g, k_i]

            # Keep top-30 by absolute value, zero out rest
            if np.count_nonzero(delta_row) > delta_top:
                abs_vals = np.abs(delta_row)
                threshold = np.partition(abs_vals, -delta_top)[-delta_top]
                delta_row[abs_vals < threshold] = 0.0

            if np.any(delta_row != 0):
                collected_rows.append(sp.csr_matrix(delta_row.reshape(1, -1)))

    if not collected_rows:
        raise ValueError("No non-zero delta rows collected for PCA")

    print(f"Collected {len(collected_rows)} sparse delta rows for PCA")

    # Stack into sparse matrix and run PCA
    X_sparse = sp.vstack(collected_rows)  # (n_rows, G_full)
    X_dense = X_sparse.toarray()

    n_components = min(max_components, X_dense.shape[0], G_full)
    pca = PCA(n_components=n_components)
    pca.fit(X_dense)

    # Find number of components for variance threshold
    cumvar = np.cumsum(pca.explained_variance_ratio_)
    d = int(np.searchsorted(cumvar, variance_threshold) + 1)
    d = min(d, max_components)

    print(f"PCA: {d} components explain {cumvar[d-1]*100:.1f}% variance")
    print(f"  Top-5 explained variance ratios: {pca.explained_variance_ratio_[:5]}")

    basis = pca.components_[:d].T.astype(np.float32)      # (G_full, d)
    explained = pca.explained_variance_[:d].astype(np.float32)  # (d,)

    return basis, explained


def compute_delta_stats(
    h5_values,      # HDF5 dataset (N, G_full, K)
    h5_indices,     # HDF5 dataset (N, G_full, K)
    cell_names,
    adata,
    G_full: int,
    n_pairs: int = 2000,
    seed: int = 42,
):
    """
    Compute per-gene delta L2 norm statistics from sparse attention.

    Returns:
        delta_mean: (G_full,) float32 — mean of per-gene delta L2 norm
        delta_std:  (G_full,) float32 — std of per-gene delta L2 norm
    """
    rng = np.random.RandomState(seed)
    name_to_idx = {name: i for i, name in enumerate(cell_names)}

    obs = adata.obs
    if "condition" in obs.columns:
        is_control = obs["condition"] == "control"
    elif "perturbation_covariates" in obs.columns:
        is_control = obs["perturbation_covariates"].str.contains("control", case=False)
    elif "treatment" in obs.columns:
        is_control = obs["treatment"] == "control"
    else:
        raise ValueError("Cannot identify control cells from adata.obs columns")

    ctrl_names = [n for n in obs.index[is_control] if n in name_to_idx]
    pert_names = [n for n in obs.index[~is_control] if n in name_to_idx]
    print(f"Delta stats: {len(ctrl_names)} control, {len(pert_names)} perturbation cells")

    n_pairs = min(n_pairs, len(ctrl_names), len(pert_names))
    ctrl_sample = rng.choice(ctrl_names, n_pairs, replace=True)
    pert_sample = rng.choice(pert_names, n_pairs, replace=True)

    # Accumulate per-gene L2 norms
    running_sum = np.zeros(G_full, dtype=np.float64)
    running_sq = np.zeros(G_full, dtype=np.float64)

    for i in tqdm(range(n_pairs), desc="Computing delta stats"):
        ci = name_to_idx[ctrl_sample[i]]
        pi = name_to_idx[pert_sample[i]]

        ctrl_vals = h5_values[ci].astype(np.float32)   # (G_full, K)
        ctrl_idx = h5_indices[ci].astype(np.int32)
        pert_vals = h5_values[pi].astype(np.float32)
        pert_idx = h5_indices[pi].astype(np.int32)

        # Compute per-gene delta L2 norm
        for g in range(G_full):
            # Build dense delta for this gene's attention row
            delta = {}

            for k_i in range(pert_idx.shape[1]):
                col = int(pert_idx[g, k_i])
                if col >= 0:
                    delta[col] = delta.get(col, 0.0) + float(pert_vals[g, k_i])

            for k_i in range(ctrl_idx.shape[1]):
                col = int(ctrl_idx[g, k_i])
                if col >= 0:
                    delta[col] = delta.get(col, 0.0) - float(ctrl_vals[g, k_i])

            if delta:
                l2 = np.sqrt(sum(v ** 2 for v in delta.values()))
                running_sum[g] += l2
                running_sq[g] += l2 ** 2

    mean = (running_sum / n_pairs).astype(np.float32)
    std = np.sqrt(np.maximum(running_sq / n_pairs - (running_sum / n_pairs) ** 2, 1e-8)).astype(np.float32)

    print(f"Delta stats from {n_pairs} pairs:")
    print(f"  mean L2 range: [{mean.min():.6f}, {mean.max():.6f}]")
    print(f"  std L2 range:  [{std.min():.6f}, {std.max():.6f}]")

    return mean, std


def verify_coverage(
    h5_values,
    h5_indices,
    cell_names,
    adata,
    G_full: int,
    n_pairs: int = 100,
    delta_top: int = 30,
    seed: int = 123,
):
    """
    Verify that K=300 sparse attn covers the true delta top-30 entries.
    """
    rng = np.random.RandomState(seed)
    name_to_idx = {name: i for i, name in enumerate(cell_names)}

    obs = adata.obs
    if "condition" in obs.columns:
        is_control = obs["condition"] == "control"
    elif "perturbation_covariates" in obs.columns:
        is_control = obs["perturbation_covariates"].str.contains("control", case=False)
    elif "treatment" in obs.columns:
        is_control = obs["treatment"] == "control"
    else:
        raise ValueError("Cannot identify control cells from adata.obs columns")

    ctrl_names = [n for n in obs.index[is_control] if n in name_to_idx]
    pert_names = [n for n in obs.index[~is_control] if n in name_to_idx]

    n_pairs = min(n_pairs, len(ctrl_names), len(pert_names))
    ctrl_sample = rng.choice(ctrl_names, n_pairs, replace=False)
    pert_sample = rng.choice(pert_names, n_pairs, replace=False)

    coverages = []

    for i in tqdm(range(n_pairs), desc="Verifying coverage"):
        ci = name_to_idx[ctrl_sample[i]]
        pi = name_to_idx[pert_sample[i]]

        ctrl_vals = h5_values[ci].astype(np.float32)
        ctrl_idx = h5_indices[ci].astype(np.int32)
        pert_vals = h5_values[pi].astype(np.float32)
        pert_idx = h5_indices[pi].astype(np.int32)

        # Sample some gene rows to check
        nonzero_rows = np.where(np.abs(ctrl_vals).sum(axis=1) > 0)[0]
        if len(nonzero_rows) == 0:
            continue
        check_genes = rng.choice(nonzero_rows, min(20, len(nonzero_rows)), replace=False)

        for g in check_genes:
            # Columns covered by union of ctrl and pert sparse entries
            covered_cols = set()
            for k_i in range(ctrl_idx.shape[1]):
                col = int(ctrl_idx[g, k_i])
                if col >= 0:
                    covered_cols.add(col)
            for k_i in range(pert_idx.shape[1]):
                col = int(pert_idx[g, k_i])
                if col >= 0:
                    covered_cols.add(col)

            # Compute full dense delta for this row
            delta = np.zeros(G_full, dtype=np.float32)
            for k_i in range(pert_idx.shape[1]):
                col = int(pert_idx[g, k_i])
                if col >= 0:
                    delta[col] += pert_vals[g, k_i]
            for k_i in range(ctrl_idx.shape[1]):
                col = int(ctrl_idx[g, k_i])
                if col >= 0:
                    delta[col] -= ctrl_vals[g, k_i]

            # Find true top-30 delta entries
            abs_delta = np.abs(delta)
            if np.count_nonzero(abs_delta) < delta_top:
                continue
            top_cols = set(np.argpartition(abs_delta, -delta_top)[-delta_top:])

            # Coverage = fraction of top-30 delta cols that are in covered_cols
            hits = len(top_cols & covered_cols)
            coverages.append(hits / delta_top)

    if coverages:
        coverages = np.array(coverages)
        print(f"\n=== Coverage Verification ===")
        print(f"Pairs checked: {n_pairs}, gene rows checked: {len(coverages)}")
        print(f"Delta top-{delta_top} coverage by K=300 sparse attn:")
        print(f"  Mean:   {coverages.mean():.4f}")
        print(f"  Median: {np.median(coverages):.4f}")
        print(f"  Min:    {coverages.min():.4f}")
        print(f"  P5:     {np.percentile(coverages, 5):.4f}")
        print(f"  P25:    {np.percentile(coverages, 25):.4f}")
    else:
        print("WARNING: No valid gene rows found for coverage check")


def main():
    parser = argparse.ArgumentParser(description="Precompute sparse attention matrices")
    parser.add_argument("--data-name", type=str, default="norman")
    parser.add_argument("--n-top-genes", type=int, default=5000)
    parser.add_argument("--fold", type=int, default=1)
    parser.add_argument("--split-method", type=str, default="additive")
    parser.add_argument("--topk", type=int, default=30)
    parser.add_argument("--use-negative-edge", action="store_true", default=True)
    parser.add_argument("--scgpt-model-dir", type=str,
                        default="transfer/data/scGPT_pretrained")
    parser.add_argument("--max-seq-len", type=int, default=5000,
                        help="scGPT max_seq_len, must be >= n_valid_genes + 1")
    parser.add_argument("--attn-layer", type=int, default=11)
    parser.add_argument("--attn-use-rank-norm", action="store_true", default=True)
    parser.add_argument("--batch-size", type=int, default=2,
                        help="Batch size for extraction (rank norm is memory-intensive)")
    parser.add_argument("--top-k", type=int, default=300,
                        help="Per-row top-K for sparse attention")
    parser.add_argument("--n-pca-pairs", type=int, default=1000,
                        help="Number of (ctrl, pert) pairs for PCA basis")
    parser.add_argument("--max-pca-components", type=int, default=64)
    parser.add_argument("--output", type=str,
                        default="cache/norman_attn_L11_sparse.h5")
    parser.add_argument("--device", type=str, default="cuda")
    args = parser.parse_args()

    device = torch.device(args.device if torch.cuda.is_available() else "cpu")
    print(f"Device: {device}")

    # === Data loading (reuse from precompute_attn_features.py) ===
    Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes()

    scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data")
    data_manager = Data(scdfm_data_path)
    data_manager.load_data(args.data_name)

    # Convert var_names if needed
    if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"):
        data_manager.adata.var_names = data_manager.adata.var["gene_name"].values
        data_manager.adata.var_names_make_unique()
        print(f"Converted var_names to gene symbols, sample: {list(data_manager.adata.var_names[:5])}")

    data_manager.process_data(
        n_top_genes=args.n_top_genes,
        split_method=args.split_method,
        fold=args.fold,
        use_negative_edge=args.use_negative_edge,
        k=args.topk,
    )

    adata = data_manager.adata
    N = adata.n_obs
    G_full = adata.n_vars
    print(f"Dataset: {N} cells × {G_full} genes")

    # === Build extractor ===
    hvg_gene_names = list(adata.var_names)
    scgpt_model_dir = os.path.join(
        os.path.dirname(_REPO_ROOT),  # transfer/
        args.scgpt_model_dir.replace("transfer/", ""),
    )
    extractor = FrozenScGPTExtractor(
        model_dir=scgpt_model_dir,
        hvg_gene_names=hvg_gene_names,
        device=device,
        max_seq_len=args.max_seq_len,
        target_std=1.0,
        warmup_batches=0,
    )
    extractor = extractor.to(device)
    extractor.eval()

    n_valid = (extractor.hvg_to_scgpt_id >= 0).sum().item()
    valid_gene_mask = (extractor.hvg_to_scgpt_id >= 0).cpu().numpy()
    K = args.top_k
    print(f"Valid genes in scGPT vocab: {n_valid}/{G_full}")
    print(f"Sequence length: {n_valid + 1} (with CLS), max_seq_len: {args.max_seq_len}")
    print(f"Top-K: {K}")

    # === Create output HDF5 ===
    output_path = os.path.join(_PROJECT_ROOT, args.output)
    os.makedirs(os.path.dirname(output_path), exist_ok=True)

    cell_names = list(adata.obs_names)

    X = adata.X
    is_sparse = hasattr(X, "toarray")

    est_gb = N * G_full * K * 4 / 1e9  # float16 + int16 = 4 bytes per entry
    print(f"Output: {output_path}")
    print(f"Estimated size: {est_gb:.1f} GB (float16 values + int16 indices)")
    print(f"Batch size: {args.batch_size}, total batches: {(N + args.batch_size - 1) // args.batch_size}")

    with h5py.File(output_path, "w") as h5:
        # Pre-allocate datasets
        val_ds = h5.create_dataset(
            "attn_values", shape=(N, G_full, K), dtype="float16",
            chunks=(1, G_full, K),
        )
        idx_ds = h5.create_dataset(
            "attn_indices", shape=(N, G_full, K), dtype="int16",
            chunks=(1, G_full, K),
        )
        h5.create_dataset("cell_names", data=np.array(cell_names, dtype="S"))
        h5.create_dataset("valid_gene_mask", data=valid_gene_mask)

        # === Step 1: Batch extraction ===
        batch_size = args.batch_size
        for start in tqdm(range(0, N, batch_size), desc="Extracting sparse attn"):
            end = min(start + batch_size, N)

            if is_sparse:
                expr_np = X[start:end].toarray()
            else:
                expr_np = X[start:end]

            expr = torch.from_numpy(expr_np.astype(np.float32)).to(device)

            with torch.no_grad():
                vals, idxs = extract_sparse_attn(
                    extractor, expr,
                    top_k=K,
                    attn_layer=args.attn_layer,
                    use_rank_norm=args.attn_use_rank_norm,
                )  # (B, G_full, K) each

            val_ds[start:end] = vals.numpy()
            idx_ds[start:end] = idxs.numpy()

        # === Step 2: PCA basis ===
        print("\nComputing PCA basis...")
        pca_basis, pca_explained = compute_pca_basis(
            val_ds, idx_ds, cell_names, adata, G_full,
            n_pairs=args.n_pca_pairs,
            max_components=args.max_pca_components,
        )
        d = pca_basis.shape[1]
        h5.create_dataset("pca_basis", data=pca_basis)             # (G_full, d)
        h5.create_dataset("pca_explained_var", data=pca_explained)  # (d,)

        # === Step 3: Delta stats ===
        print("\nComputing delta statistics...")
        delta_mean, delta_std = compute_delta_stats(
            val_ds, idx_ds, cell_names, adata, G_full,
        )
        h5.create_dataset("delta_mean", data=delta_mean)
        h5.create_dataset("delta_std", data=delta_std)

    # === Step 4: Verify coverage ===
    print("\nVerifying coverage...")
    with h5py.File(output_path, "r") as h5:
        verify_coverage(
            h5["attn_values"], h5["attn_indices"],
            cell_names, adata, G_full,
        )

    print(f"\nDone! Output saved to {output_path}")
    print(f"  attn_values:  ({N}, {G_full}, {K}) float16")
    print(f"  attn_indices: ({N}, {G_full}, {K}) int16")
    print(f"  pca_basis:    ({G_full}, {d}) float32")
    print(f"  delta_mean/std: ({G_full},) float32")


if __name__ == "__main__":
    main()