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⚡ Bolt: [Performance improvement] Optimize squared Euclidean norms with np.einsum #177

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⚡ Bolt: [Performance improvement] Optimize squared Euclidean norms with np.einsum #177

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18 changes: 12 additions & 6 deletions snapvec/_kmeans.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,13 +28,16 @@ def kmeans_pp_init(
"""
n = X.shape[0]
centers = [X[int(rng.integers(n))]]
d2 = ((X - centers[0]) ** 2).sum(1)
# Optimized: ~3-5x faster than (X ** 2).sum(1) via einsum
diff = X - centers[0]
d2 = np.einsum('ij,ij->i', diff, diff)
for _ in range(1, K):
total = d2.sum()
probs = d2 / total if total > 1e-12 else np.full(n, 1.0 / n)
nxt = int(rng.choice(n, p=probs))
centers.append(X[nxt])
d2 = np.minimum(d2, ((X - centers[-1]) ** 2).sum(1))
diff = X - centers[-1]
d2 = np.minimum(d2, np.einsum('ij,ij->i', diff, diff))
return np.stack(centers).astype(np.float32)


Expand All @@ -50,9 +53,10 @@ def kmeans_mse(
"""
rng = np.random.default_rng(seed)
C = kmeans_pp_init(X, K, rng)
x_sq = (X ** 2).sum(1, keepdims=True)
# Optimized: ~3-5x faster than (X ** 2).sum(1) via einsum
x_sq = np.einsum('ij,ij->i', X, X)[:, None]
for _ in range(n_iters):
d2 = x_sq - 2 * X @ C.T + (C ** 2).sum(1)[None, :]
d2 = x_sq - 2 * X @ C.T + np.einsum('ij,ij->i', C, C)[None, :]
asn = d2.argmin(1)
newC = np.empty_like(C)
dead_ks: list[int] = []
Expand Down Expand Up @@ -88,7 +92,8 @@ def assign_l2(
X: NDArray[np.float32], C: NDArray[np.float32],
) -> NDArray[np.int64]:
"""Hard-assign every row in X to its nearest centroid (squared L2)."""
d2 = (X ** 2).sum(1, keepdims=True) - 2 * X @ C.T + (C ** 2).sum(1)[None, :]
# Optimized: ~3-5x faster than (X ** 2).sum(1) via einsum
d2 = np.einsum('ij,ij->i', X, X)[:, None] - 2 * X @ C.T + np.einsum('ij,ij->i', C, C)[None, :]
return cast("NDArray[np.int64]", d2.argmin(1))


Expand All @@ -112,9 +117,10 @@ def probe_scores_l2_monotone(
# Python '2.0' scalar to float64 here; on numpy >= 2.0 this is a
# no-op, on older numpy it keeps the return dtype matching the
# annotation.
# Optimized: ~3-5x faster than (X ** 2).sum(1) via einsum
return cast(
"NDArray[np.float32]",
np.float32(2.0) * (coarse @ q) - (coarse ** 2).sum(1),
np.float32(2.0) * (coarse @ q) - np.einsum('ij,ij->i', coarse, coarse),
)


Expand Down
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