Many machine learning tasks are invariant under transformations such as translations and rotations. The $G$-bispectrum is a complete invariant that determines a signal up to a group action, but its computation is costly and existing implementations each cover a single group. We present bispectrum, an open-source, fully unit-tested PyTorch library providing efficient, differentiable selective $G$-bispectra for seven group actions. Selectivity reduces the number of coefficients from $O(|G|^2)$ to $O(|G|)$ for finite groups, from $O(m^{5/2})$ to $O(m)$ for planar rotations on the disk with $m$ harmonics, and from $Θ(L^3)$ to $Θ(L^2)$ for spherical 3D rotations using our augmented selective spherical bispectrum at band limit $L$. The library is invariant up to floating-point error and computes selective $G$-bispectra in sub-millisecond time on GPU. We evaluate them as pooling layers on PatchCamelyon and OrganMNIST3D against five baselines. Across 46 comparisons, bispectral pooling remains competitive, matching all baselines on PCam and improving on max pooling by 5.0 percentage points on OrganMNIST3D. On Spherical MNIST, our method with an MLP head reaches 95.8% on rotated test digits, matching a spherical CNN at $4\times$ lower latency and coming within 0.3 percentage points of a tensor-product network at $364\times$ lower latency. Selective $G$-bispectra provide fast, competitive invariant layers for finite and continuous groups.

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