find_dupes: streaming row-chunked compare (fits ~4GB cgroup caps)

The single-matrix fix still oom-killed at n=31,937: Hermes background
jobs run in a systemd scope capped near 4GB, and the n^2 uint64 xor
matrix alone is 8GB. Now row chunks of 1024 stream through both hash
gates (peak a few hundred MB temps), the only resident n^2 structure
is the bool close-matrix (1GB), and the color-histogram L1 is computed
only for rows surviving both hashes (replaces the 4GB float32 matrix).
Verified on a 2,502-file corpus incl planted cross-chunk duplicate
pair and an orphan copy (correctly untouched).

find_dupes.py

@@ -111,21 +111,18 @@ def main():
             print(f"  {i+1}/{len(files)}", flush=True)
 
     names = list(data)
-    # strict (complete-linkage) clustering — NO transitive chaining:
-    # seed with the ungrouped image having most neighbours, take its
-    # within-threshold neighbourhood, then keep only members pairwise
-    # within threshold. Union-find chaining merges unrelated frames
-    # after enough hops (verified failure: 90-frame 'group' of six
-    # different scenes).
     n = len(names)
-    print(f"Pairwise compare {n}x{n} (numpy hamming) ...")
+    print(f"Pairwise compare {n}x{n} (streaming hamming, chunked) ...")
 
-    def hamming64(mat_u64):
-        """Full pairwise Hamming, memory-safe at 30k+ scale. The old
-        64-pass int64 shift-add peaked at ~27 GB on 32k frames (OOM);
-        bitwise_count (numpy>=2.3) or a per-byte popcount LUT keeps the
-        peak at one uint64 xor matrix + a uint8 result (max dist 64)."""
-        x = mat_u64[:, None] ^ mat_u64[None, :]
+    # Memory-safe at 30k+ scale under ~4 GB cgroup caps (verified: the
+    # full n^2 uint64 xor matrix — 8 GB at n=32k — got a background run
+    # oom-killed in a hermes-worker systemd scope on 2026-10-29). Row
+    # chunks of 1024 keep peak temps at a few hundred MB; only the final
+    # bool close-matrix (n^2 bytes) stays resident. uint8 distances
+    # suffice (max 64) via np.bitwise_count (numpy>=2.3) or a per-byte
+    # popcount LUT.
+    def hamming_chunk(mat, rows):
+        x = mat[rows, None] ^ mat[None, :]
         if hasattr(np, "bitwise_count"):
             ham = np.bitwise_count(x)
         else:
@@ -139,22 +136,28 @@ def main():
         return ham
 
     dmat = np.array([data[f][0] for f in names], dtype=np.uint64)
-    close = hamming64(dmat) <= args.max_dist
+    amat = np.array([data[f][1] for f in names], dtype=np.uint64)
+    max_a = args.max_adist if args.max_adist is not None else 8
+    CH = 1024
+    close = np.zeros((n, n), dtype=bool)
+    for s in range(0, n, CH):
+        rows = slice(s, min(s + CH, n))
+        close[rows] = ((hamming_chunk(dmat, rows) <= args.max_dist)
+                       & (hamming_chunk(amat, rows) <= max_a))
+        if (s // CH) % 8 == 0:
+            print(f"  hashes {min(s + CH, n)}/{n}", flush=True)
+    del dmat, amat
 
     # combined close matrix: dHash AND aHash AND color-histogram gates.
     # dHash alone has a false-positive floor (verified: distinct frames
     # at distance <=10); a second, differently-computed hash plus a
     # color signature is what separates 'same frame replayed' from
-    # 'same show, different shot'.
-    amat = np.array([data[f][1] for f in names], dtype=np.uint64)
-    close &= hamming64(amat) <= (args.max_adist
-                                 if args.max_adist is not None else 8)
-
+    # 'same show, different shot'. Color L1 is computed per row only for
+    # rows that survived both hashes — few rows at real corpus scale,
+    # and it replaces the old n^2 float32 matrix (4 GB at n=32k).
     hmat = np.stack([data[f][3] for f in names])  # N x 192 hist rows
-    cdist = np.zeros((n, n), dtype=np.float32)
-    for i in range(n):  # row-wise keeps it ~seconds and RAM-light
-        cdist[i] = np.abs(hmat - hmat[i]).sum(axis=1)
-    close &= cdist <= args.max_cdist
+    for i in np.flatnonzero(close.any(axis=1)):
+        close[i] &= np.abs(hmat - hmat[i]).sum(axis=1) <= args.max_cdist
     np.fill_diagonal(close, False)
 
     # strict (complete-linkage) clustering — NO transitive chaining: