train/CCFM/pca_emb/src/data/data.py

"""
Data loading for grn_svd.
Imports scDFM Data/PerturbationDataset by temporarily swapping sys.modules
so that scDFM's 'src.*' packages are visible during import.
"""

import sys
import os

import torch
from torch.utils.data import Dataset

_SCDFM_ROOT = os.path.normpath(
    os.path.join(os.path.dirname(__file__), "..", "..", "..", "..", "transfer", "code", "scDFM")
)

# Cache to avoid repeated imports
_cached_classes = {}


def get_data_classes():
    """Lazily import scDFM data classes with proper module isolation."""
    if _cached_classes:
        return (
            _cached_classes["Data"],
            _cached_classes["PerturbationDataset"],
            _cached_classes["TrainSampler"],
            _cached_classes["TestDataset"],
        )

    # Save CCFM's src modules
    saved = {}
    for key in list(sys.modules.keys()):
        if key == "src" or key.startswith("src."):
            saved[key] = sys.modules.pop(key)

    # Ensure __init__.py exists for scDFM data_process
    for d in ["src", "src/data_process", "src/utils", "src/tokenizer"]:
        init_path = os.path.join(_SCDFM_ROOT, d, "__init__.py")
        if not os.path.exists(init_path):
            os.makedirs(os.path.dirname(init_path), exist_ok=True)
            with open(init_path, "w") as f:
                f.write("# Auto-created by CCFM\n")

    sys.path.insert(0, _SCDFM_ROOT)
    try:
        from src.data_process.data import Data, PerturbationDataset, TrainSampler, TestDataset
        _cached_classes["Data"] = Data
        _cached_classes["PerturbationDataset"] = PerturbationDataset
        _cached_classes["TrainSampler"] = TrainSampler
        _cached_classes["TestDataset"] = TestDataset
    finally:
        # Remove scDFM's src.* entries
        for key in list(sys.modules.keys()):
            if (key == "src" or key.startswith("src.")) and not key.startswith("scdfm_"):
                del sys.modules[key]

        # Restore CCFM's src modules
        for key, mod in saved.items():
            sys.modules[key] = mod

        if _SCDFM_ROOT in sys.path:
            sys.path.remove(_SCDFM_ROOT)

    return Data, PerturbationDataset, TrainSampler, TestDataset


class GRNDatasetWrapper(Dataset):
    """
    Wraps scDFM PerturbationDataset to produce sparse delta triplets.

    Returns delta_values (B, G_sub, K) and delta_indices (B, G_sub, K)
    instead of dense z_target (B, G_sub, G_sub).
    SVD projection happens on GPU in denoiser.train_step().
    """

    def __init__(self, base_dataset, sparse_cache, gene_ids_cpu, infer_top_gene):
        self.base = base_dataset          # scDFM PerturbationDataset
        self.sparse_cache = sparse_cache  # SparseDeltaCache (multi-process safe)
        self.gene_ids = gene_ids_cpu      # (G_full,) CPU tensor — vocab-encoded gene IDs
        self.infer_top_gene = infer_top_gene

    def __len__(self):
        return len(self.base)

    def __getitem__(self, idx):
        batch = self.base[idx]

        # 1. Random gene subset
        G_full = batch["src_cell_data"].shape[-1]
        input_gene_ids = torch.randperm(G_full)[:self.infer_top_gene]

        # 2. Sparse cache lookup → sparse triplets (runs in worker process)
        src_names = batch["src_cell_id"]
        tgt_names = batch["tgt_cell_id"]
        if src_names and isinstance(src_names[0], (tuple, list)):
            src_names = [n[0] for n in src_names]
            tgt_names = [n[0] for n in tgt_names]
        delta_values, delta_indices = self.sparse_cache.lookup_delta(
            src_names, tgt_names, input_gene_ids, device=torch.device("cpu")
        )  # delta_values: (B, G_sub, K), delta_indices: (B, G_sub, K) int16

        # 3. Subset expression data
        return {
            "src_cell_data": batch["src_cell_data"][:, input_gene_ids],   # (B, G_sub)
            "tgt_cell_data": batch["tgt_cell_data"][:, input_gene_ids],   # (B, G_sub)
            "condition_id": batch["condition_id"],                         # (B, 2)
            "delta_values": delta_values,                                  # (B, G_sub, K)
            "delta_indices": delta_indices,                                # (B, G_sub, K) int16
            "gene_ids_sub": self.gene_ids[input_gene_ids],                 # (G_sub,)
            "input_gene_ids": input_gene_ids,                              # (G_sub,)
        }