add Gram matrix fast path for pairwise_distance

pg_gpu/decomposition.py

@@ -368,26 +368,55 @@ def pairwise_distance(haplotype_matrix: HaplotypeMatrix,
         matrix.transfer_to_gpu()
 
     hap = matrix.haplotypes
+    from ._memutil import dac_and_n
+
+    _, n_valid = dac_and_n(hap)
 
     if missing_data == 'exclude':
-        missing_per_var = cp.sum(hap < 0, axis=0)
-        complete = missing_per_var == 0
+        complete = n_valid == hap.shape[0]
         hap = hap[:, complete]
-
-    X = cp.where(hap >= 0, hap, 0).astype(cp.float64)
-    valid_mask = (hap >= 0).astype(cp.float64)
-    has_missing = cp.any(hap < 0)
-    n = X.shape[0]
+        has_missing = False
+    else:
+        has_missing = bool(cp.any(n_valid < hap.shape[0]).get())
+    n = hap.shape[0]
+    m = hap.shape[1]
 
     if metric in ('euclidean', 'sqeuclidean', 'cityblock'):
+        # Fast path: chunked Gram trick for euclidean/sqeuclidean
+        # without missing data.  Never materializes the full (n, m)
+        # float64 matrix -- only (n, chunk_size) slices at a time.
+        if not has_missing and metric in ('euclidean', 'sqeuclidean'):
+            from ._memutil import (estimate_variant_chunk_size,
+                                   free_gpu_pool)
+            chunk_size = estimate_variant_chunk_size(
+                n, bytes_per_element=8, n_intermediates=2)
+            G = cp.zeros((n, n), dtype=cp.float64)
+            for col_start in range(0, m, chunk_size):
+                col_end = min(col_start + chunk_size, m)
+                X_chunk = hap[:, col_start:col_end].astype(cp.float64)
+                G += X_chunk @ X_chunk.T
+                del X_chunk
+            free_gpu_pool()
+            # d²(i,j) = ||x_i||² + ||x_j||² - 2*x_i·x_j
+            norms_sq = cp.diag(G)
+            D2 = norms_sq[:, None] + norms_sq[None, :] - 2.0 * G
+            D2 = cp.maximum(D2, 0.0)
+            idx_i, idx_j = cp.triu_indices(n, k=1)
+            d = D2[idx_i, idx_j]
+            if metric == 'euclidean':
+                d = cp.sqrt(d)
+            return d.get()
+
+        # General batched path (missing data or cityblock).
+        # Needs X and valid_mask, but these are accessed by row pairs
+        # so the full matrix is required.
+        X = cp.where(hap >= 0, hap, 0).astype(cp.float64)
+        valid_mask = (hap >= 0).astype(cp.float64)
         idx_i, idx_j = cp.triu_indices(n, k=1)
         n_pairs = len(idx_i)
 
-        # Estimate batch size from available GPU memory
-        n_variants = X.shape[1]
         free_mem = cp.cuda.Device().mem_info[0]
-        # Each pair needs ~3 float64 arrays of n_variants (diff, joint, result)
-        bytes_per_pair = n_variants * 8 * 3
+        bytes_per_pair = m * 8 * 3
         batch_size = max(1, min(n_pairs, int(free_mem * 0.3 / bytes_per_pair)))
         dist_parts = []
 
@@ -397,20 +426,18 @@ def pairwise_distance(haplotype_matrix: HaplotypeMatrix,
             bj = idx_j[start:end]
 
             if has_missing:
-                # only compare at jointly-valid sites
                 joint = valid_mask[bi] * valid_mask[bj]
                 n_joint = cp.sum(joint, axis=1)
             else:
-                n_joint = cp.float64(X.shape[1])
+                n_joint = cp.float64(m)
 
             if metric == 'cityblock':
                 raw = cp.sum(cp.abs(X[bi] - X[bj]) * (joint if has_missing else 1.0), axis=1)
             else:
                 raw = cp.sum(((X[bi] - X[bj]) ** 2) * (joint if has_missing else 1.0), axis=1)
 
-            # normalize by jointly-valid sites
             if has_missing:
-                d = cp.where(n_joint > 0, raw * X.shape[1] / n_joint, 0.0)
+                d = cp.where(n_joint > 0, raw * m / n_joint, 0.0)
             else:
                 d = raw
 
@@ -421,6 +448,7 @@ def pairwise_distance(haplotype_matrix: HaplotypeMatrix,
         return cp.concatenate(dist_parts).get()
     else:
         from scipy.spatial.distance import pdist
+        X = cp.where(hap >= 0, hap, 0).astype(cp.float64)
         X_cpu = X.get()
         return pdist(X_cpu, metric=metric)
 

tests/test_pbs_decomposition.py

@@ -214,6 +214,25 @@ def test_vs_scipy(self):
             np.testing.assert_allclose(dist_pg, dist_scipy, rtol=1e-10,
                                       err_msg=f"{metric} mismatch")
 
+    def test_missing_data_modes(self):
+        from scipy.spatial.distance import pdist
+        hap = np.array([
+            [0, 0, 1, -1],
+            [0, 1, 1, 0],
+            [1, 1, 0, 1],
+        ], dtype=np.int8)
+        matrix = HaplotypeMatrix(hap, np.arange(4) * 1000, 0, 4000)
+
+        excluded = decomposition.pairwise_distance(
+            matrix, metric='sqeuclidean', missing_data='exclude')
+        expected = pdist(hap[:, :3].astype(float), metric='sqeuclidean')
+        np.testing.assert_allclose(excluded, expected, rtol=1e-10)
+
+        included = decomposition.pairwise_distance(
+            matrix, metric='sqeuclidean', missing_data='include')
+        np.testing.assert_allclose(included, [4.0 / 3.0, 4.0, 3.0],
+                                   rtol=1e-10)
+
 
 # ---------------------------------------------------------------------------
 # PCoA tests