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SparseDeltaCache — Returns sparse delta triplets (indices, values) for SVD projection.
Each gene row's delta attention is computed across ALL G_full=5035 columns,
then per-row top-K sparsification selects the K most important interactions.
The SVD projection (delta @ W) happens on GPU, not here.
Multi-process safe: each DataLoader worker lazily opens its own HDF5 handle.
HDF5 layout (from precompute_sparse_attn.py):
/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
"""
import os
import h5py
import numpy as np
import torch
def _read_sparse_batch(h5_values, h5_indices, name_to_idx,
src_cell_names, tgt_cell_names, gene_idx_np=None):
"""
Shared HDF5 reading logic for sparse caches.
Returns:
src_vals, src_idxs, tgt_vals, tgt_idxs: numpy arrays (B, G_sub, K)
"""
seen = {}
unique_names = []
for n in src_cell_names + tgt_cell_names:
if n not in seen:
seen[n] = len(unique_names)
unique_names.append(n)
unique_h5_idx = [name_to_idx[n] for n in unique_names]
sorted_order = np.argsort(unique_h5_idx)
sorted_h5_idx = [unique_h5_idx[i] for i in sorted_order]
raw_vals = h5_values[sorted_h5_idx]
raw_idxs = h5_indices[sorted_h5_idx]
unsort = np.argsort(sorted_order)
raw_vals = raw_vals[unsort]
raw_idxs = raw_idxs[unsort]
if gene_idx_np is not None:
raw_vals = raw_vals[:, gene_idx_np, :]
raw_idxs = raw_idxs[:, gene_idx_np, :]
src_map = [seen[n] for n in src_cell_names]
tgt_map = [seen[n] for n in tgt_cell_names]
return raw_vals[src_map], raw_idxs[src_map], raw_vals[tgt_map], raw_idxs[tgt_map]
class SparseDeltaCache:
"""
Returns sparse delta triplets for GPU-side SVD projection.
Lookup flow:
1. Read src/tgt sparse attention: (G_full, K=300) values + indices
2. Select gene subset rows
3. Scatter to dense: (B, G_sub, G_full) — chunked to avoid OOM
4. Delta = tgt_dense - src_dense (full G_full columns, NOT G_sub)
5. Per-row top-K on G_full columns
6. Return (delta_values, delta_indices) sparse triplets
"""
def __init__(self, h5_path, delta_top_k=30):
self.h5_path = h5_path
self.delta_top_k = delta_top_k
# Read metadata only, then close — safe for fork
with h5py.File(h5_path, "r") as h5:
self.G_full = h5["attn_values"].shape[1]
self.K_sparse = h5["attn_values"].shape[2]
cell_names = h5["cell_names"].asstr()[:]
self.name_to_idx = {name: i for i, name in enumerate(cell_names)}
if "valid_gene_mask" in h5:
self.valid_gene_mask = h5["valid_gene_mask"][:].astype(bool)
else:
self.valid_gene_mask = np.ones(self.G_full, dtype=bool)
# Per-process HDF5 handle (lazily opened)
self._h5 = None
self._attn_values = None
self._attn_indices = None
self._pid = None
print(f" SparseDeltaCache: {len(self.name_to_idx)} cells, "
f"G_full={self.G_full}, K_sparse={self.K_sparse}, delta_topk={self.delta_top_k}")
print(f" valid genes: {self.valid_gene_mask.sum()}/{self.G_full}")
def _ensure_h5_open(self):
"""Ensure current process has its own HDF5 file handle."""
pid = os.getpid()
if self._h5 is None or self._pid != pid:
if self._h5 is not None:
try:
self._h5.close()
except Exception:
pass
self._h5 = h5py.File(self.h5_path, "r")
self._attn_values = self._h5["attn_values"]
self._attn_indices = self._h5["attn_indices"]
self._pid = pid
def get_missing_gene_mask(self, gene_indices=None):
"""
Return missing gene mask (True = missing/invalid).
Pure numpy operation — no HDF5 I/O needed.
"""
mask = torch.from_numpy(~self.valid_gene_mask) # True = missing
if gene_indices is not None:
return mask[gene_indices.cpu()]
return mask
def lookup_delta(self, src_cell_names, tgt_cell_names, gene_indices, device=None):
"""
Compute sparse delta attention triplets for SVD projection.
Args:
src_cell_names: list of str, control cell identifiers
tgt_cell_names: list of str, perturbation cell identifiers
gene_indices: (G_sub,) tensor, gene subset row indices
device: target torch device (usually CPU for DataLoader workers)
Returns:
delta_values: (B, G_sub, delta_topk) float32 — top-K delta values per row
delta_indices: (B, G_sub, delta_topk) int16 — column indices in G_full space
"""
self._ensure_h5_open()
if device is None:
device = torch.device("cpu")
B = len(src_cell_names)
gene_idx_np = gene_indices.cpu().numpy()
G_sub = len(gene_idx_np)
K = self.delta_top_k
# Read sparse data from HDF5 (uses per-process handle)
# gene_idx_np selects ROWS only — we keep all G_full columns
sv_np, si_np, tv_np, ti_np = _read_sparse_batch(
self._attn_values, self._attn_indices, self.name_to_idx,
src_cell_names, tgt_cell_names, gene_idx_np)
src_vals = torch.from_numpy(sv_np.astype(np.float32)).to(device) # (B, G_sub, K_sparse)
src_idxs = torch.from_numpy(si_np.astype(np.int64)).to(device)
tgt_vals = torch.from_numpy(tv_np.astype(np.float32)).to(device)
tgt_idxs = torch.from_numpy(ti_np.astype(np.int64)).to(device)
# Output sparse triplets
out_values = torch.zeros(B, G_sub, K, device=device)
out_indices = torch.zeros(B, G_sub, K, dtype=torch.int16, device=device)
# Process in chunks (100 rows per chunk) to limit memory
chunk_size = 100
for c_start in range(0, G_sub, chunk_size):
c_end = min(c_start + chunk_size, G_sub)
sv = src_vals[:, c_start:c_end, :] # (B, c_len, K_sparse)
si = src_idxs[:, c_start:c_end, :]
tv = tgt_vals[:, c_start:c_end, :]
ti = tgt_idxs[:, c_start:c_end, :]
c_len = c_end - c_start
# Scatter sparse entries to dense attention rows: (B, c_len, G_full)
src_dense = torch.zeros(B, c_len, self.G_full, device=device)
tgt_dense = torch.zeros(B, c_len, self.G_full, device=device)
src_dense.scatter_(-1, si, sv)
tgt_dense.scatter_(-1, ti, tv)
# Delta on FULL G_full columns (no column subsetting!)
delta = tgt_dense - src_dense # (B, c_len, G_full)
# Per-row top-K on G_full columns
_, topk_idx = delta.abs().topk(K, dim=-1) # (B, c_len, K)
topk_vals = delta.gather(-1, topk_idx) # (B, c_len, K)
out_values[:, c_start:c_end, :] = topk_vals
out_indices[:, c_start:c_end, :] = topk_idx.short()
return out_values, out_indices # (B, G_sub, K) float32, (B, G_sub, K) int16
def close(self):
if self._h5 is not None:
try:
self._h5.close()
except Exception:
pass
self._h5 = None
self._attn_values = None
self._attn_indices = None
def __del__(self):
self.close()
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